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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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[ build-system ]
requires = [ "setuptools" ]
build-backend = "setuptools.build_meta"
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[ project . urls ]
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homepage = "https://huggingface.co/lerobot"
documentation = "https://huggingface.co/docs/lerobot/index"
source = "https://github.com/huggingface/lerobot"
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issues = "https://github.com/huggingface/lerobot/issues"
discord = "https://discord.gg/s3KuuzsPFb"
[ project ]
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name = "lerobot"
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version = "0.4.3"
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description = "🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch"
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dynamic = [ "readme" ]
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license = { text = "Apache-2.0" }
requires-python = ">=3.10"
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authors = [
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{ name = "Rémi Cadène" , email = "re.cadene@gmail.com" } ,
{ name = "Simon Alibert" , email = "alibert.sim@gmail.com" } ,
{ name = "Alexander Soare" , email = "alexander.soare159@gmail.com" } ,
{ name = "Quentin Gallouédec" , email = "quentin.gallouedec@ec-lyon.fr" } ,
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{ name = "Steven Palma" , email = "imstevenpmwork@ieee.org" } ,
{ name = "Pepijn Kooijmans" , email = "pepijnkooijmans@outlook.com" } ,
{ name = "Michel Aractingi" , email = "michel.aractingi@gmail.com" } ,
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{ name = "Adil Zouitine" , email = "adilzouitinegm@gmail.com" } ,
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{ name = "Dana Aubakirova" , email = "danaaubakirova17@gmail.com" } ,
{ name = "Caroline Pascal" , email = "caroline8.pascal@gmail.com" } ,
{ name = "Martino Russi" , email = "nopyeps@gmail.com" } ,
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{ name = "Thomas Wolf" , email = "thomaswolfcontact@gmail.com" } ,
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]
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classifiers = [
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"Development Status :: 3 - Alpha" ,
"Intended Audience :: Developers" ,
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"Intended Audience :: Education" ,
"Intended Audience :: Science/Research" ,
"License :: OSI Approved :: Apache Software License" ,
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"Programming Language :: Python :: 3.10" ,
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"Topic :: Software Development :: Build Tools" ,
"Topic :: Scientific/Engineering :: Artificial Intelligence" ,
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]
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keywords = [ "lerobot" , "huggingface" , "robotics" , "machine learning" , "artificial intelligence" ]
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dependencies = [
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# Hugging Face dependencies
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"datasets>=4.0.0,<4.2.0" ,
"diffusers>=0.27.2,<0.36.0" ,
"huggingface-hub[hf-transfer,cli]>=0.34.2,<0.36.0" ,
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"accelerate>=1.10.0,<2.0.0" ,
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# Core dependencies
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"setuptools>=71.0.0,<81.0.0" ,
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"cmake>=3.29.0.1,<4.2.0" ,
"einops>=0.8.0,<0.9.0" ,
"opencv-python-headless>=4.9.0,<4.13.0" ,
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"av>=15.0.0,<16.0.0" ,
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"jsonlines>=4.0.0,<5.0.0" ,
"packaging>=24.2,<26.0" ,
"pynput>=1.7.7,<1.9.0" ,
"pyserial>=3.5,<4.0" ,
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"wandb>=0.20.0,<0.22.0" , # TODO: Bumb dependency (compatible with protobuf)
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"torch>=2.2.1,<2.8.0" , # TODO: Bumb dependency
"torchcodec>=0.2.1,<0.6.0; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')" , # TODO: Bumb dependency
"torchvision>=0.21.0,<0.23.0" , # TODO: Bumb dependency
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"draccus==0.10.0" , # TODO: Remove ==
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"gymnasium>=1.1.1,<2.0.0" ,
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"rerun-sdk>=0.24.0,<0.27.0" ,
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# Support dependencies
"deepdiff>=7.0.1,<9.0.0" ,
"imageio[ffmpeg]>=2.34.0,<3.0.0" ,
"termcolor>=2.4.0,<4.0.0" ,
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]
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# Optional dependencies
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[ project . optional-dependencies ]
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# Common
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pygame-dep = [ "pygame>=2.5.1,<2.7.0" ]
placo-dep = [ "placo>=0.9.6,<0.10.0" ]
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transformers-dep = [ "transformers>=4.57.1,<5.0.0" ]
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grpcio-dep = [ "grpcio==1.73.1" , "protobuf==6.31.0" ] # TODO: Bumb dependency (compatible with wandb)
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# Motors
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feetech = [ "feetech-servo-sdk>=1.0.0,<2.0.0" ]
dynamixel = [ "dynamixel-sdk>=3.7.31,<3.9.0" ]
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# Robots
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gamepad = [ "lerobot[pygame-dep]" , "hidapi>=0.14.0,<0.15.0" ]
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hopejr = [ "lerobot[feetech]" , "lerobot[pygame-dep]" ]
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lekiwi = [ "lerobot[feetech]" , "pyzmq>=26.2.1,<28.0.0" ]
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unitree_g1 = [
"pyzmq>=26.2.1,<28.0.0" ,
"onnxruntime>=1.16.0"
]
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reachy2 = [ "reachy2_sdk>=1.0.14,<1.1.0" ]
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kinematics = [ "lerobot[placo-dep]" ]
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intelrealsense = [
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"pyrealsense2>=2.55.1.6486,<2.57.0 ; sys_platform != 'darwin'" ,
"pyrealsense2-macosx>=2.54,<2.55.0 ; sys_platform == 'darwin'" ,
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]
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phone = [ "hebi-py>=2.8.0,<2.12.0" , "teleop>=0.1.0,<0.2.0" , "fastapi<1.0" ]
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# Policies
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wallx = [
"transformers==4.49.0" ,
"peft==0.17.1" ,
"scipy==1.15.3" ,
"torchdiffeq==0.2.5" ,
"qwen_vl_utils==0.0.11"
]
Add OpenPi, Pi0 and Pi0.5 (#1910)
* initial commit
* change device in test
* do detailed import
* adhere to python 3.11 syntax
* fix autodocstring
* additionally
* do same in other files
* add model. prefix to all keys in state dict
* use dummy stats
* add pi05
* also shorten action_steps
* fix test
* all test pass! and fix tokenizer max length between 05 and 0
* remove test
* fix transformer dependency
* fix test
* split pi0 and pi05 policy in seperate files
* fix test
* fix push to hub test
* add some comments, license and readme
* remove warning in config
* add pi05 to factory
* remove check
* rename action_horizon to chunk_size
* clean up padding of state and action (more in line with lerobot pi0)
* add openpi image transforms for training and add more flexibility to _preprocess_images similar to lerobot pi0
* fix key match from pytorch state dict (similar keys to openpi implementation now)
* also for pi05
* update to python 3.11
* revert to openpi transformer replace python 3.11
* fix(modeling pi0): nit warning message
* use safeauto_docstring
* fix: remove unused param
* fix from pretrained
* add preprocess tests
* also compile forward method
* Do not add model prefix to normalization
* use same name for action and state dim as lerobot pi0 and remove fixed image keys
* load from pretrained_path
* temp: hardcode base model
* fix override self.pretrained_path = None overwrite
* rename to loss
* remove additional image augmentations, lerobot dataset already does this
* Add docs
* put tests in test folder
* Add test to instatiate all base models
* go back to python 3.10
* update docs
* adapt docs pi05
* change docs: finetune base model options
* minor docs fixes and dependencies
* remove todo
* cast float64 to float32 for mps
* skip if no transformers
* fix tests
* add new models to modelcard
* add back init
* fix circular input
* feat: only run pi test on GPU
* remove require_nightly_gpu
* replace decorator test_pi0_openpi
* rename action_dim, state_dim to max_action_dim, max_state_dim
* fix doc and constants
* cleanup tests
* fix from pretrained
* fix tests
* add comment pi0 pi05 tests, add image features to pi0 pi05 hub tests
* fix, state is included in language not in flow head
* Move test to specific folder
* and paligemma task with newline
* remove add_special_tokens, not needed
* feedback pr
* Remove previous pi0 and rename pi0_openpi and pi05_openpi
* Add Quantile stats to LeRobotDataset (#1985)
* - Add RunningQuantileStats class for efficient histogram-based quantile computation
- Integrate quantile parameters (compute_quantiles, quantiles) into LeRobotDataset
- Support quantile computation during episode collection and aggregation
- Add comprehensive function-based test suite (24 tests) for quantile functionality
- Maintain full backward compatibility with existing stats computation
- Enable configurable quantiles (default: [0.01, 0.99]) for robust normalization
* style fixes, make quantiles computation by default to new datasets
* fix tests
* - Added DEFAULT_QUANTILES=[0.01, 0.10, 0.50, 0.90, 0.99] to be computed for each features instead of being chosen by the user
- Fortified tests.
* - add helper functions to reshape stats
- add missing test for quantiles
* - Add QUANTILE normalization mode to normalize the data with the 1st and 99th percentiles.
- Add QUANTILE10 normalization mode to normalize the data with the 10th and 90th percentiles.
* style fixes
* Added missing lisence
* Simplify compute_stats
* - added script `augment_dataset_quantile_stats.py` so that we can add quantile stats to existing v3 datasets that dont have quatniles
- modified quantile computation instead of using the edge for the value, interpolate the values in the bin
* rename pi0/pi05 files
* Remove open pi patch and use custom transformer branch for now
* renaming
* fix
* Revert "fix"
This reverts commit 1ea65730ac2cbca6e5869df734fbd4392561b3c6.
* fix naming
* feet(pi0/pi0.5): add pipeline (#2009)
* feat(processor): convert openpi model with processor
* TODO: Make test works
* fix(modeling_pi0openpi): update attention mask value and time scaling; improve task handling in tests
- Changed the attention mask value from `self.config.attention_mask_value` to a fixed value of `-2.3819763e38`.
- Updated time scaling in the `sample_noise` method to use a constant factor of `0.999` and an offset of `0.001`.
- Enhanced task handling in tests to ensure proper formatting and batch size consistency.
- Cleaned up commented-out test code for clarity.
* refactor(pi0): rename PI0OpenPIConfig and PI0OpenPIPolicy to PI0Config and PI0Policy
- Updated imports and references throughout the codebase to reflect the new naming convention.
- Introduced a new processor file for PI0 to handle pre-processing and post-processing steps.
- Adjusted tests to utilize the renamed classes, ensuring consistency and functionality.
- Enhanced clarity and maintainability by removing outdated naming conventions.
* refactor(pi05): rename PI0OpenPIPolicy to PI0Policy and update configuration
- Renamed `PI0OpenPIPolicy` to `PI0Policy` for consistency with naming conventions.
- Updated the `PI05OpenPIConfig` to include a new `tokenizer_max_length` attribute and changed the normalization mode for state from `MEAN_STD` to `QUANTILES`.
- Simplified model initialization in `PI05OpenPIPolicy` by removing unused `dataset_stats` parameter.
- Added a new processor class for `Pi05PrepareStateTokenizerProcessorStep` with `@dataclass` for improved readability.
- Introduced a test script to compare the integration of the PI0OpenPI policy with the original implementation, ensuring local testing compatibility.
* feat(processor): convert openpi model with processor
* TODO: Make test works
* fix(modeling_pi0openpi): update attention mask value and time scaling; improve task handling in tests
- Changed the attention mask value from `self.config.attention_mask_value` to a fixed value of `-2.3819763e38`.
- Updated time scaling in the `sample_noise` method to use a constant factor of `0.999` and an offset of `0.001`.
- Enhanced task handling in tests to ensure proper formatting and batch size consistency.
- Cleaned up commented-out test code for clarity.
* refactor(pi0): rename PI0OpenPIConfig and PI0OpenPIPolicy to PI0Config and PI0Policy
- Updated imports and references throughout the codebase to reflect the new naming convention.
- Introduced a new processor file for PI0 to handle pre-processing and post-processing steps.
- Adjusted tests to utilize the renamed classes, ensuring consistency and functionality.
- Enhanced clarity and maintainability by removing outdated naming conventions.
* refactor(pi05): rename PI0OpenPIPolicy to PI0Policy and update configuration
- Renamed `PI0OpenPIPolicy` to `PI0Policy` for consistency with naming conventions.
- Updated the `PI05OpenPIConfig` to include a new `tokenizer_max_length` attribute and changed the normalization mode for state from `MEAN_STD` to `QUANTILES`.
- Simplified model initialization in `PI05OpenPIPolicy` by removing unused `dataset_stats` parameter.
- Added a new processor class for `Pi05PrepareStateTokenizerProcessorStep` with `@dataclass` for improved readability.
- Introduced a test script to compare the integration of the PI0OpenPI policy with the original implementation, ensuring local testing compatibility.
* refactor(pi05): update imports and rename configuration classes
- Changed imports to reflect the new naming convention for PI05 configuration and policy classes.
- Renamed `PI05OpenPIConfig` to `PI05Config` and `PI05OpenPIPolicy` to `PI05Policy` for consistency.
- Introduced a new processor file for PI05, implementing pre-processing and post-processing steps.
- Updated tests to utilize the renamed classes, ensuring functionality and consistency across the codebase.
* update(pi05): increase tokenizer_max_length for improved processing
- Changed the `tokenizer_max_length` from 48 to 200 to enhance the model's capability in handling longer sequences.
- This adjustment aims to improve the overall performance and flexibility of the PI05 configuration.
* add default for state (max_state_dim)
* correct naming
* fix import
* cleanup code
* remove unused test
* us quantiles for action
* move to device
* remove discrete state assert
* fix pi05 test
* move pi05 to device
* use base models in comparison tests
* small renames for tests
* change number of tokens pi05 test
* fix openpi tokenization in test
* fix hub test
* fix test
* assert lerobot vs openpi tests
---------
Co-authored-by: Pepijn <pepijn@huggingface.co>
* add headers
* add back previously removed imports
* update if statement load processor with dataset stats
* remove to avoid circular import
* inject dataset stats for pretrained models
* check normalization before applying
* add link to quantile augument script
* fix(policies): transformers import for ci in PI0 & PI05 (#2039)
* fix(policies): transformers import for ci in PI0
* fix(policies): transformers import for ci in PI05
* test(processor): fix expected raise when normalization types are missing (#2040)
* switch normalization order pipeline for pi05
* Fix/quantiles script (#2064)
* refactor augment stats with quantiles script
add parallelization for faster processing
shift the quantile normalization between -1 1
* fix replay buffer tests
* fix comment
* overwrite the pipeline normalization features with the policy features
* remove double normalization overwrite
* cleanup from pretrained
* remove typo
* also set norm_map
* fix(augment_quantiles) images incorrectly divided by 255
* clamp quantiles
* link to lerobot base models
* rename tests
* encorperate PR feedback
* update docstring for RunningQuantileStats
* update doc links
* Revert "clamp quantiles"
This reverts commit 172207471c8f2cb62958e9a9e6a0535ba3ff67d4.
* fix self.paligemma
* fix tests related to quantiles that were scaled to [0,1], the new range is [-1, 1]
* fix libero doc and use different transformer branch
* use fix branch instead of feat
* update results libero
* add new line
* fix formatting
* precommit
* update results libero
* update libero doc
* update title
* final changes
* add quantiles to test
* run pre commit
---------
Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>
Co-authored-by: Adil Zouitine <adilzouitinegm@gmail.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: Steven Palma <steven.palma@huggingface.co>
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pi = [ "transformers @ git+https://github.com/huggingface/transformers.git@fix/lerobot_openpi" ]
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smolvla = [ "lerobot[transformers-dep]" , "num2words>=0.5.14,<0.6.0" , "accelerate>=1.7.0,<2.0.0" , "safetensors>=0.4.3,<1.0.0" ]
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groot = [
"lerobot[transformers-dep]" ,
"peft>=0.13.0,<1.0.0" ,
"dm-tree>=0.1.8,<1.0.0" ,
"timm>=1.0.0,<1.1.0" ,
"safetensors>=0.4.3,<1.0.0" ,
"Pillow>=10.0.0,<13.0.0" ,
"decord>=0.6.0,<1.0.0; (platform_machine == 'AMD64' or platform_machine == 'x86_64')" ,
"ninja>=1.11.1,<2.0.0" ,
"flash-attn>=2.5.9,<3.0.0 ; sys_platform != 'darwin'"
]
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sarm = [ "lerobot[transformers-dep]" , "faker>=33.0.0,<35.0.0" , "matplotlib>=3.10.3,<4.0.0" , "qwen-vl-utils>=0.0.14" ]
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xvla = [ "lerobot[transformers-dep]" ]
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hilserl = [ "lerobot[transformers-dep]" , "gym-hil>=0.1.13,<0.2.0" , "lerobot[grpcio-dep]" , "lerobot[placo-dep]" ]
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# Features
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async = [ "lerobot[grpcio-dep]" , "matplotlib>=3.10.3,<4.0.0" ]
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# Development
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dev = [ "pre-commit>=3.7.0,<5.0.0" , "debugpy>=1.8.1,<1.9.0" , "lerobot[grpcio-dep]" , "grpcio-tools==1.73.1" , "mypy>=1.19.1" ]
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test = [ "pytest>=8.1.0,<9.0.0" , "pytest-timeout>=2.4.0,<3.0.0" , "pytest-cov>=5.0.0,<8.0.0" , "mock-serial>=0.0.1,<0.1.0 ; sys_platform != 'win32'" ]
video_benchmark = [ "scikit-image>=0.23.2,<0.26.0" , "pandas>=2.2.2,<2.4.0" ]
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# Simulation
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aloha = [ "gym-aloha>=0.1.2,<0.2.0" ]
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pusht = [ "gym-pusht>=0.1.5,<0.2.0" , "pymunk>=6.6.0,<7.0.0" ] # TODO: Fix pymunk version in gym-pusht instead
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libero = [ "lerobot[transformers-dep]" , "hf-libero>=0.1.3,<0.2.0" ]
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metaworld = [ "metaworld==3.0.0" ]
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# All
all = [
"lerobot[dynamixel]" ,
"lerobot[gamepad]" ,
"lerobot[hopejr]" ,
"lerobot[lekiwi]" ,
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"lerobot[reachy2]" ,
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"lerobot[kinematics]" ,
"lerobot[intelrealsense]" ,
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# "lerobot[wallx]",
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# "lerobot[pi]", TODO(Pepijn): Update pi to transformers v5
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"lerobot[smolvla]" ,
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# "lerobot[groot]", TODO(Steven): Gr00t requires specific installation instructions for flash-attn
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"lerobot[xvla]" ,
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"lerobot[hilserl]" ,
"lerobot[async]" ,
"lerobot[dev]" ,
"lerobot[test]" ,
"lerobot[video_benchmark]" ,
"lerobot[aloha]" ,
"lerobot[pusht]" ,
feat(processors): use pipelines across the codebase (#1452)
* Refactor observation preprocessing to use a modular pipeline system
- Introduced `RobotPipeline` and `ObservationProcessor` for handling observation transformations.
- Updated `preprocess_observation` to maintain backward compatibility while leveraging the new pipeline.
- Added tests for the new processing components and ensured they match the original functionality.
- Removed hardcoded logic in favor of a more flexible, composable architecture.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Refactor observation processing and improve modularity
- Updated `ObservationProcessor` to enhance the modular design for processing observations.
- Cleaned up imports and improved code readability by removing unnecessary lines and comments.
- Ensured backward compatibility while integrating new processing components.
- Added tests to validate the functionality of the updated processing architecture.
* Remove redundant tests for None observation and serialization methods in `test_observation_processor.py` to streamline the test suite and improve maintainability.
* Refactor processing architecture to use RobotProcessor
- Replaced instances of RobotPipeline with RobotProcessor across the codebase for improved modularity and clarity.
- Introduced ProcessorStepRegistry for better management of processing steps.
- Updated relevant documentation and tests to reflect the new processing structure.
- Enhanced the save/load functionality to support the new processor design.
- Added a model card template for RobotProcessor to facilitate sharing and documentation.
* Add RobotProcessor tutorial to documentation
- Introduced a new tutorial on using RobotProcessor for preprocessing robot data.
- Added a section in the table of contents for easy navigation to the new tutorial.
- The tutorial covers key concepts, real-world scenarios, and practical examples for effective use of the RobotProcessor pipeline.
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* Add normalization processor and related components
- Introduced `NormalizationProcessor` to handle both observation normalization and action unnormalization.
- Added `ObservationNormalizer` and `ActionUnnormalizer` classes for specific normalization tasks.
- Updated `__init__.py` to include the new `NormalizationProcessor` in the module exports.
- Enhanced `ObservationProcessor` with registration in the `ProcessorStepRegistry` for better modularity.
- Created `RenameProcessor` for renaming keys in observations, improving flexibility in data processing.
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* Enhance processing architecture with new components
- Added `RenameProcessor` to facilitate key renaming in observations, improving data handling flexibility.
- Updated `__init__.py` to include `RenameProcessor` in module exports.
- Refactored `NormalizationProcessor` and `ObservationNormalizer` to use `rsplit` for better key handling.
- Introduced comprehensive tests for `NormalizationProcessor` and `RenameProcessor` to ensure functionality and robustness.
* chore (docs): add docstring for processor
* fix (test): test factory
* fix(test): policies
* Update tests/processor/test_observation_processor.py
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Signed-off-by: Adil Zouitine <adilzouitinegm@gmail.com>
* chore(test): add suggestion made by copilot regarding numpy test
* fix(test): import issue
* Refactor normalization components and update tests
- Renamed `ObservationNormalizer` to `NormalizerProcessor` and `ActionUnnormalizer` to `UnnormalizerProcessor` for clarity.
- Consolidated normalization logic for both observations and actions into `NormalizerProcessor` and `UnnormalizerProcessor`.
- Updated tests to reflect the new class names and ensure proper functionality of normalization and unnormalization processes.
- Enhanced handling of missing statistics in normalization processes.
* chore (docstrin):Improve docstring for NormalizerProcessor
* feat (device processor): Implement device processor
* chore (batch handling): Enhance processing components with batch conversion utilities
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* fix(test): linting issue
* chore (output format): improves output format
* chore (type): add typing for multiprocess envs
* feat (overrides): Implement support for loading processors with parameter overrides
- Added the ability to provide non-serializable objects when loading processors from saved configurations using the `overrides` parameter.
- Enhanced error handling for invalid override keys and instantiation errors.
- Updated documentation and examples to illustrate the usage of overrides for both registered and unregistered steps.
- Added comprehensive tests to validate the new functionality and ensure backward compatibility.
* chore(normalization): addressing comments from copilot
* chore(learner): nit comment from copilot
* feat(pipeline): Enhance step_through method to support both tuple and dict inputs
* refactor(pipeline): Simplify observation and padding data handling in batch transitions
* Apply suggestions from code review
Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com>
Signed-off-by: Adil Zouitine <adilzouitinegm@gmail.com>
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* refactor(pipeline): Introduce ComplementaryDataProcessor for handling complementary data in transitions
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* refactor(pipeline): Transition from tuple to dictionary format for EnvTransition
- Updated the EnvTransition structure to use a dictionary format instead of a tuple, enhancing readability and maintainability.
- Replaced instances of TransitionIndex with TransitionKey for accessing transition components.
- Adjusted related processing functions and tests to accommodate the new dictionary format, ensuring consistent handling of transitions across the codebase.
* refactor(observation_processor): Improve observation processing by using constants and simplifying pixel handling
- Introduced constants for observation keys to enhance readability.
- Streamlined the handling of the "pixels" key by copying observations first and processing images more clearly.
- Updated the environment state and agent position assignments to use the new constants, improving maintainability.
* feat(pipeline): Add hook unregistration functionality and enhance documentation
- Implemented methods to unregister before, after, and reset hooks in the RobotProcessor class, allowing for more flexible hook management.
- Enhanced documentation to clarify hook execution semantics and the implications of modifying transitions within hooks.
- Added comprehensive tests to verify the correct behavior of hook registration and unregistration, including error handling for non-existent hooks.
* refactor(pipeline): Clarify hook behavior and improve documentation
- Updated the RobotProcessor class to ensure hooks are strictly for observation and do not modify transitions, enhancing clarity and maintainability.
- Refactored hook registration methods to reflect the new behavior, ensuring they accept only functions that do not return modified transitions.
- Enhanced documentation to clearly outline the purpose of hooks and their execution semantics.
- Added tests to verify that hooks are not executed during the step_through method while ensuring they function correctly during the __call__ method.
* feat(pipeline): Add __repr__ method to RobotProcessor for improved readability
- Implemented a __repr__ method in the RobotProcessor class to provide a clear string representation of the processor, including step names and optional parameters like name and seed.
- Added comprehensive tests to validate the __repr__ output for various scenarios, including empty processors, single and multiple steps, custom names, and seed values.
- Ensured that the representation handles long lists of steps with truncation for better readability.
* chore(pipeline): Move _CFG_NAME along other class member
* refactor(pipeline): Utilize get_safe_torch_device for device assignment
- Replaced direct torch.device instantiation with get_safe_torch_device to ensure safe device handling.
- This change enhances code readability and maintains consistency in device management across the RobotProcessor class.
* refactor(pipeline): Enhance state filename generation and profiling method
- Updated state filename generation to use the registry name when available, improving clarity in saved files.
- Modified the profile_steps method to include a warmup_runs parameter, allowing for more controlled performance profiling.
- Ensured consistent conditions during profiling by deep copying transitions for each run, enhancing accuracy in timing results.
* chore(doc): address pip install commant lerobot that not exist yet
* feat(pipeline): Enhance configuration filename handling and state file naming
- Introduced support for custom configuration filenames in the `save_pretrained` method, allowing users to specify a filename instead of the default.
- Improved state file naming to include step indices, preventing conflicts when multiple processors of the same type are saved.
- Added automatic detection for configuration files when loading from a directory, with error handling for multiple files.
- Updated tests to validate new features, including custom filenames and automatic config detection.
* refactor(pipeline): Improve state file naming conventions for clarity and uniqueness
- Enhanced state file naming to include the processor's sanitized name, ensuring uniqueness when multiple processors are saved in the same directory.
- Updated tests to reflect changes in state file naming, verifying that filenames now include the processor name and step indices to prevent conflicts.
- Added a new test to validate state file naming when using multiple processors, ensuring distinct filenames for each processor's state files.
* docs(pipeline): Add clarification for repo name sanitization process
* Feat/pipeline add feature contract (#1637)
* Add feature contract to pipelinestep and pipeline
* Add tests
* Add processor tests
* PR feedback
* encorperate pr feedback
* type in doc
* oops
* docs(pipeline): Clarify transition handling and hook behavior
- Updated documentation to specify that hooks always receive transitions in EnvTransition format, ensuring consistent behavior across input formats.
- Refactored the step_through method to yield only EnvTransition objects, regardless of the input format, and updated related tests to reflect this change.
- Enhanced test assertions to verify the structure of results and the correctness of processing steps.
* refactor(pipeline): Remove to() method for device management
- Eliminated the to() method from RobotProcessor, which was responsible for moving tensor states to specified devices.
- Removed associated unit tests that validated the functionality of the to() method across various scenarios.
- Streamlined the pipeline code by focusing on other device management strategies.
* refactor(pipeline): Remove model card generation and streamline processor methods
- Eliminated the _generate_model_card method from RobotProcessor, which was responsible for generating README.md files from a template.
- Updated save_pretrained method to remove model card generation, focusing on serialization of processor definitions and parameters.
- Added default implementations for get_config, state_dict, load_state_dict, reset, and feature_contract methods in various processor classes to enhance consistency and usability.
* refactor(observation): Streamline observation preprocessing and remove unused processor methods
- Updated the `preprocess_observation` function to enhance image handling and ensure proper tensor formatting.
- Removed the `RobotProcessor` and associated transition handling from the `rollout` function, simplifying the observation processing flow.
- Integrated direct calls to `preprocess_observation` for improved clarity and efficiency in the evaluation script.
* refactor(pipeline): Rename parameters for clarity and enhance save/load functionality
- Updated parameter names in the save_pretrained and from_pretrained methods for improved readability, changing destination_path to save_directory and source to pretrained_model_name_or_path.
- Enhanced the save_pretrained method to ensure directory creation and file handling is consistent with the new parameter names.
- Streamlined the loading process in from_pretrained to utilize loaded_config for better clarity and maintainability.
* refactor(pipeline): minor improvements (#1684)
* chore(pipeline): remove unused features + device torch + envtransition keys
* refactor(pipeline): ImageProcessor & StateProcessor are both implemented directly in VanillaObservationPRocessor
* refactor(pipeline): RenameProcessor now inherits from ObservationProcessor + remove unused code
* test(pipeline): fix broken test after refactors
* docs(pipeline): update docstrings VanillaObservationProcessor
* chore(pipeline): move None check to base pipeline classes
* feat(processors): Introduce processors for various policy types
- Added `make_processor` function to create processor instances for different policy types, including `tdmpc`, `diffusion`, `act`, `vqbet`, `pi0`, `pi0fast`, `sac`, and `reward_classifier`.
- Implemented corresponding processor files for each policy type, encapsulating normalization and unnormalization steps.
- Updated existing policies to remove direct normalization dependencies, enhancing modularity and clarity.
- Enhanced test coverage to validate the integration of new processors with existing policy configurations.
* refactor(learner): Remove normalization from cached image features retrieval
- Simplified the retrieval of observation features by removing the normalization step from the `get_cached_image_features` method calls.
- This change enhances clarity and aligns with the recent updates to policy processors.
* refactor(policies): Remove unnormalization step from action predictions
- Eliminated the unnormalization of actions in both `TDMPCPolicy` and `VQBeTPolicy` classes to streamline action prediction.
- This change improves code clarity and aligns with recent updates to policy processors.
* feat(train): Integrate preprocessor into training pipeline
* refactor(train): Update preprocessor initialization to include dataset statistics
* refactor(policies): Enhance processor creation and add NaN detection hook
* feat(record): Integrate RobotProcessor into recording loop and update policy handling
- Added support for RobotProcessor in the record_loop function to enhance data processing capabilities.
- Updated the logic to reset both policy and processor when provided, ensuring proper state management.
- Modified action prediction to utilize the processor, improving the overall functionality of the recording process.
- Adjusted the save_checkpoint function to include preprocessor state saving, enhancing checkpointing capabilities.
* feat(migration): Add script for migrating policy models with normalization layers
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* feat(migrate): Enhance migration script to create preprocessor and postprocessor for policy models
- Updated the migration script to generate both a preprocessor and a postprocessor, improving the handling of normalization for training and inference.
- Added functionality to convert features to PolicyFeature objects, ensuring compatibility with the new processor architecture.
- Refined the extraction and removal of normalization statistics and layers, streamlining the migration process.
- Improved error handling for missing mandatory configuration fields during model instantiation.
* feat(migrate): Add model card generation and saving to migration script
- Implemented functionality to generate and save a model card for the migrated model, including metadata such as dataset repository ID, license, and tags.
- Enhanced the script to push the model card to the hub if requested, improving model documentation and accessibility.
- Refactored the saving process to ensure the model card is saved locally and uploaded correctly when pushing to the hub.
* feat(processor): Introduce ToBatchProcessor for handling observation batching
- Added ToBatchProcessor to ensure observations have proper batch dimensions for model processing.
- Implemented functionality to add batch dimensions to state and image observations as needed.
- Created comprehensive unit tests to validate the processor's behavior with various tensor dimensions and types.
- Ensured compatibility with existing transition keys and maintained the integrity of non-observation data.
* feat(processors): Add ToBatchProcessor to multiple policy processors
- Integrated ToBatchProcessor into various policy processors to handle observation batching.
- Updated make functions for act, diffusion, pi0, pi0fast, sac, smolvla, tdmpc, and vqbet processors to include the new batching functionality.
- Ensured consistency across all processor implementations for improved data handling.
* refactor(factory): Remove unused imports and NaN detection hook from processor creation
* feat(batch_processor): Enhance ToBatchProcessor to handle action batching
- Updated ToBatchProcessor to add batch dimensions to actions in addition to observations.
- Implemented separate methods for processing observations and actions, improving code readability.
- Added comprehensive unit tests to validate action batching functionality across various tensor dimensions and types.
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* feat(factory): Enhance make_processor to support preprocessor and postprocessor configuration
- Introduced ProcessorConfigKwargs TypedDict for better type safety in processor configuration.
- Updated make_processor to accept preprocessor and postprocessor configuration filenames, improving flexibility in processor instantiation.
- Refactored the loading of pretrained processors to utilize the new configuration options.
* refactor(factory): Clean up imports in factory.py
- Removed unused import of IdentityProcessor to streamline the code.
* feat(migrate): Extend load_model_from_hub to include train configuration
- Updated load_model_from_hub to return the train configuration alongside the model state_dict and config.
- Modified main function to handle the additional train configuration when loading models from both the hub and local paths.
- Adjusted dataset_repo_id extraction to utilize the train configuration for improved accuracy.
* refactor(record): Rename processor parameters and update processing logic
- Renamed `processor` to `preprocessor` and added `postprocessor` parameter for clarity.
- Updated the `record_loop` and `predict_action` functions to utilize the new preprocessor and postprocessor, enhancing the processing flow.
- Ensured compatibility with existing functionality while improving code readability.
* feat(batch_processor): Add task field processing to ToBatchProcessor
- Enhanced ToBatchProcessor to wrap string tasks in a list, adding batch dimensions for compatibility with model inference.
- Implemented a new method for processing complementary data, ensuring that task values are correctly handled as either strings or lists of strings.
- Added comprehensive unit tests to validate task processing, including edge cases and in-place mutation of complementary data.
* feat(normalization): Implement IDENTITY mode for normalization and unnormalization
- Enhanced NormalizerProcessor and UnnormalizerProcessor to support IDENTITY mode, allowing features to bypass normalization when specified.
- Updated processing logic to check normalization modes and handle missing statistics gracefully.
- Added comprehensive unit tests to validate IDENTITY mode functionality for both observations and actions, ensuring correct behavior across various scenarios.
- Improved error handling for unsupported normalization modes.
* fix(rebase): remove residual normalization layer:
* refactor(diffusion): remove normalization layer from input processing
* refactor(normalization): Remove unused state dict transformation methods and streamline imports
- Eliminated the _transform_state_dict_keys and _load_as_safetensor methods from PI0Policy, simplifying the model loading process.
- Cleaned up imports in modeling_pi0.py by removing log_model_loading_keys and init_logging.
- Updated TDMPCPolicy and VQBeTPolicy to handle action removal from batches during offline evaluation.
- Introduced hotswap_stats function in normalize_processor.py to update normalization statistics dynamically, with corresponding tests to ensure functionality.
* refactor(normalization): Clean up imports in normalize_processor.py
* feat(batch_processor): Add feature_contract method to ToBatchProcessor
- Introduced feature_contract method that returns features without modification, maintaining the no-op behavior of the processor.
- This addition enhances the flexibility of the ToBatchProcessor for future feature processing needs.
* fix(dependencies): Update transformers dependency constraint to allow only versions up to 4.52.0
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* feature(pipeline): port tokenizer pipeline for VLA (#1645)
* feat(tokenizer): Introduce TokenizerProcessor for text tokenization
- Added TokenizerProcessor class to handle tokenization of task strings using Hugging Face's AutoTokenizer.
- Supports both string and list inputs, with customizable parameters for task key, output key, and tokenization settings.
- Implemented comprehensive unit tests to validate functionality, including handling of various input scenarios and integration with RobotProcessor.
- Updated types.py to include LANGUAGE feature type and modified __init__.py to register the new processor.
* feat(language): Enhance language processing in TokenizerProcessor
- Added OBS_LANGUAGE constant to define the observation language key.
- Updated TokenizerProcessor to store tokenized task data in the observation dictionary, ensuring compatibility with the new language feature.
- Introduced Pi0NewLineProcessor to append newlines to tasks for proper tokenization.
- Modified tests to validate the integration of language tokens and attention masks in the observation structure.
* feat(tokenizer): Add padding configuration to TokenizerProcessor
- Introduced `padding_side` parameter to the TokenizerProcessor for customizable padding direction.
- Updated the `make_pi0_processor` function to include the new padding configuration.
- Enhanced unit tests to validate the functionality of the `padding_side` parameter in various scenarios.
* feat(processor): Add state management methods to Pi0NewLineProcessor
* feat(normalization): Track normalization and unnormalization info in complementary data
- Updated NormalizerProcessor and UnnormalizerProcessor to accept additional parameters for tracking normalization modes.
- Enhanced the __call__ methods to store normalization and unnormalization information in the complementary data of transitions.
- Added unit tests to verify the correct tracking of normalization info, including scenarios with missing stats and selective normalization keys.
* feat(factory): Add preprocessor and postprocessor overrides to ProcessorConfigKwargs
- Updated ProcessorConfigKwargs to include optional overrides for preprocessor and postprocessor configurations.
- Enhanced the make_processor function to utilize the new overrides, allowing for more flexible processor initialization.
* feat(processors): Integrate RenameProcessor into various processor configurations
- Added RenameProcessor to the input steps of multiple processor functions, including make_act_processor, make_diffusion_processor, make_pi0_processor, make_sac_processor, make_tdmpc_processor, make_vqbet_processor, and make_smolvla_processor.
- Consolidated normalization features from input and output into a single NormalizerProcessor for improved efficiency.
- Updated the input steps to ensure compatibility with the new RenameProcessor integration.
* feat(smolvla): Refactor language processing and introduce new line processor (#1658)
- Removed the prepare_language method and directly accessed language tokens and masks from the batch using the OBS_LANGUAGE constant.
- Added SmolVLANewLineProcessor to ensure tasks end with a newline, enhancing tokenization compatibility.
- Updated the make_smolvla_processor function to include the new line processor and tokenizer processor for improved input handling.
* feture(policies): add device processor (#1659)
* feat(processors): Integrate DeviceProcessor into multiple processor configurations
- Added DeviceProcessor to the input and output steps of various processor functions, including make_act_processor, make_diffusion_processor, make_pi0_processor, make_pi0fast_processor, make_sac_processor, make_tdmpc_processor, make_vqbet_processor, and make_smolvla_processor.
- Enhanced the DeviceProcessor class with state management methods and ensured compatibility with existing processor pipelines.
- Introduced unit tests for DeviceProcessor to validate functionality across different scenarios, including CPU and CUDA operations.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* refactor(pipeline): Remove to() method for device management
- Eliminated the to() method from RobotProcessor, which was responsible for moving tensor states to specified devices.
- Removed associated unit tests that validated the functionality of the to() method across various scenarios.
- Streamlined the pipeline code by focusing on other device management strategies.
* feat(processor): Enhance DeviceProcessor with float dtype conversion
- Added support for optional float dtype conversion in DeviceProcessor, allowing tensors to be converted to specified floating-point types while preserving non-float types.
- Implemented validation for float dtype input and updated the processor's configuration methods to include float dtype.
- Refactored tensor processing logic to streamline device movement and dtype conversion.
- Introduced comprehensive unit tests to validate the new float dtype functionality across various scenarios.
* feat(policies): Add new line processors and update module exports
* feat(processor): Enhance batch and device processors to handle index and task_index fields
- Added logic to ToBatchProcessor for unsqueezing 0D tensors for index and task_index fields, ensuring they are processed as 1D tensors.
- Updated DeviceProcessor to process index and task_index fields in complementary data, preserving their tensor types and ensuring non-tensor fields remain unchanged.
- Enhanced unit tests to validate the correct handling of index and task_index fields across various scenarios, including device compatibility and dtype preservation.
* refactor(processors): Standardize processor naming conventions
- Updated processor names across various files to use a consistent "robot_preprocessor" and "robot_postprocessor" format.
- Modified the make_processor functions in factory, act, diffusion, pi0, pi0fast, sac, smolvla, tdmpc, and vqbet to reflect the new naming scheme.
- Enhanced the pipeline configuration to align with the updated processor names, improving clarity and maintainability.
* refactor(factory): Update processor configuration and type hints
- Changed return type of get_policy_class to type[PreTrainedPolicy] for improved type safety.
- Enhanced make_processor function to utilize dataset_stats in processor creation for better flexibility.
- Updated ProcessorConfigKwargs to include dataset_stats, allowing for more comprehensive processor configurations.
- Streamlined processor initialization by removing unnecessary kwargs and ensuring clarity in processor type handling.
* refactor(factory, pi0fast): Update processor function names and parameters
- Renamed make_pi0_processor to make_pi0fast_processor for clarity and consistency.
- Updated parameter names in the factory's make_processor function to use pretrained_model_name_or_path instead of source, enhancing readability and alignment with naming conventions.
* fix(train.py) push postprocessor with preprocessor
- Add preprocesser policy overrides for device and rename_map
- Add rename_map to DatasetRecordConfig (record.py)
* refactor(device_processor): Update device handling and improve type hints
- Changed device attribute type from torch.device to str for better clarity.
- Introduced a private _device attribute to store the actual torch.device instance.
- Updated tests to conditionally check for CUDA availability, ensuring compatibility across different environments.
- Refactored device-related assertions in tests to use a consistent approach for device type verification.
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* test(tokenizer_processor): Add require_package decorator for transformers
- Introduced @require_package("transformers") decorator in multiple test functions to ensure the transformers package is available before running tests.
- This change enhances test reliability by preventing failures due to missing dependencies.
* refactor(migrate_policy_normalization): Enhance preprocessor and postprocessor structure
- Introduced RenameProcessor in the preprocessor to handle renaming features.
- Combined input and output features in a single NormalizerProcessor for improved efficiency.
- Updated RobotProcessor initialization to clarify step naming for preprocessor and postprocessor.
- Added DeviceProcessor to both preprocessor and postprocessor for better device management.
* Integrate pipeline and add phone teleop (#1681)
* Add normalization processor and related components
- Introduced `NormalizationProcessor` to handle both observation normalization and action unnormalization.
- Added `ObservationNormalizer` and `ActionUnnormalizer` classes for specific normalization tasks.
- Updated `__init__.py` to include the new `NormalizationProcessor` in the module exports.
- Enhanced `ObservationProcessor` with registration in the `ProcessorStepRegistry` for better modularity.
- Created `RenameProcessor` for renaming keys in observations, improving flexibility in data processing.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Enhance processing architecture with new components
- Added `RenameProcessor` to facilitate key renaming in observations, improving data handling flexibility.
- Updated `__init__.py` to include `RenameProcessor` in module exports.
- Refactored `NormalizationProcessor` and `ObservationNormalizer` to use `rsplit` for better key handling.
- Introduced comprehensive tests for `NormalizationProcessor` and `RenameProcessor` to ensure functionality and robustness.
* chore (docs): add docstring for processor
* fix (test): test factory
* fix(test): policies
* Update tests/processor/test_observation_processor.py
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Signed-off-by: Adil Zouitine <adilzouitinegm@gmail.com>
* chore(test): add suggestion made by copilot regarding numpy test
* fix(test): import issue
* Refactor normalization components and update tests
- Renamed `ObservationNormalizer` to `NormalizerProcessor` and `ActionUnnormalizer` to `UnnormalizerProcessor` for clarity.
- Consolidated normalization logic for both observations and actions into `NormalizerProcessor` and `UnnormalizerProcessor`.
- Updated tests to reflect the new class names and ensure proper functionality of normalization and unnormalization processes.
- Enhanced handling of missing statistics in normalization processes.
* chore (docstrin):Improve docstring for NormalizerProcessor
* feat (device processor): Implement device processor
* chore (batch handling): Enhance processing components with batch conversion utilities
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix(test): linting issue
* chore (output format): improves output format
* chore (type): add typing for multiprocess envs
* feat (overrides): Implement support for loading processors with parameter overrides
- Added the ability to provide non-serializable objects when loading processors from saved configurations using the `overrides` parameter.
- Enhanced error handling for invalid override keys and instantiation errors.
- Updated documentation and examples to illustrate the usage of overrides for both registered and unregistered steps.
- Added comprehensive tests to validate the new functionality and ensure backward compatibility.
* chore(normalization): addressing comments from copilot
* chore(learner): nit comment from copilot
* feat(pipeline): Enhance step_through method to support both tuple and dict inputs
* refactor(pipeline): Simplify observation and padding data handling in batch transitions
* Apply suggestions from code review
Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com>
Signed-off-by: Adil Zouitine <adilzouitinegm@gmail.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* refactor(pipeline): Introduce ComplementaryDataProcessor for handling complementary data in transitions
* fix(ci): temporary fix on dataset deps version
* feat(processors): Introduce processors for various policy types
- Added `make_processor` function to create processor instances for different policy types, including `tdmpc`, `diffusion`, `act`, `vqbet`, `pi0`, `pi0fast`, `sac`, and `reward_classifier`.
- Implemented corresponding processor files for each policy type, encapsulating normalization and unnormalization steps.
- Updated existing policies to remove direct normalization dependencies, enhancing modularity and clarity.
- Enhanced test coverage to validate the integration of new processors with existing policy configurations.
* refactor(learner): Remove normalization from cached image features retrieval
- Simplified the retrieval of observation features by removing the normalization step from the `get_cached_image_features` method calls.
- This change enhances clarity and aligns with the recent updates to policy processors.
* refactor(policies): Remove unnormalization step from action predictions
- Eliminated the unnormalization of actions in both `TDMPCPolicy` and `VQBeTPolicy` classes to streamline action prediction.
- This change improves code clarity and aligns with recent updates to policy processors.
* feat(train): Integrate preprocessor into training pipeline
* refactor(train): Update preprocessor initialization to include dataset statistics
* refactor(policies): Enhance processor creation and add NaN detection hook
* refactor(train): Update memory pinning logic for mps compatibility
* feat: initial commit phone teleop
* ugly delta control
* use quaternion
* Refactor observation preprocessing to use a modular pipeline system
- Introduced `RobotPipeline` and `ObservationProcessor` for handling observation transformations.
- Updated `preprocess_observation` to maintain backward compatibility while leveraging the new pipeline.
- Added tests for the new processing components and ensured they match the original functionality.
- Removed hardcoded logic in favor of a more flexible, composable architecture.
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* Refactor observation processing and improve modularity
- Updated `ObservationProcessor` to enhance the modular design for processing observations.
- Cleaned up imports and improved code readability by removing unnecessary lines and comments.
- Ensured backward compatibility while integrating new processing components.
- Added tests to validate the functionality of the updated processing architecture.
* Remove redundant tests for None observation and serialization methods in `test_observation_processor.py` to streamline the test suite and improve maintainability.
* Refactor processing architecture to use RobotProcessor
- Replaced instances of RobotPipeline with RobotProcessor across the codebase for improved modularity and clarity.
- Introduced ProcessorStepRegistry for better management of processing steps.
- Updated relevant documentation and tests to reflect the new processing structure.
- Enhanced the save/load functionality to support the new processor design.
- Added a model card template for RobotProcessor to facilitate sharing and documentation.
* Add RobotProcessor tutorial to documentation
- Introduced a new tutorial on using RobotProcessor for preprocessing robot data.
- Added a section in the table of contents for easy navigation to the new tutorial.
- The tutorial covers key concepts, real-world scenarios, and practical examples for effective use of the RobotProcessor pipeline.
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* Add normalization processor and related components
- Introduced `NormalizationProcessor` to handle both observation normalization and action unnormalization.
- Added `ObservationNormalizer` and `ActionUnnormalizer` classes for specific normalization tasks.
- Updated `__init__.py` to include the new `NormalizationProcessor` in the module exports.
- Enhanced `ObservationProcessor` with registration in the `ProcessorStepRegistry` for better modularity.
- Created `RenameProcessor` for renaming keys in observations, improving flexibility in data processing.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Enhance processing architecture with new components
- Added `RenameProcessor` to facilitate key renaming in observations, improving data handling flexibility.
- Updated `__init__.py` to include `RenameProcessor` in module exports.
- Refactored `NormalizationProcessor` and `ObservationNormalizer` to use `rsplit` for better key handling.
- Introduced comprehensive tests for `NormalizationProcessor` and `RenameProcessor` to ensure functionality and robustness.
* chore (docs): add docstring for processor
* fix (test): test factory
* fix(test): policies
* Update tests/processor/test_observation_processor.py
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Signed-off-by: Adil Zouitine <adilzouitinegm@gmail.com>
* chore(test): add suggestion made by copilot regarding numpy test
* fix(test): import issue
* Refactor normalization components and update tests
- Renamed `ObservationNormalizer` to `NormalizerProcessor` and `ActionUnnormalizer` to `UnnormalizerProcessor` for clarity.
- Consolidated normalization logic for both observations and actions into `NormalizerProcessor` and `UnnormalizerProcessor`.
- Updated tests to reflect the new class names and ensure proper functionality of normalization and unnormalization processes.
- Enhanced handling of missing statistics in normalization processes.
* chore (docstrin):Improve docstring for NormalizerProcessor
* feat (device processor): Implement device processor
* chore (batch handling): Enhance processing components with batch conversion utilities
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix(test): linting issue
* chore (output format): improves output format
* chore (type): add typing for multiprocess envs
* feat (overrides): Implement support for loading processors with parameter overrides
- Added the ability to provide non-serializable objects when loading processors from saved configurations using the `overrides` parameter.
- Enhanced error handling for invalid override keys and instantiation errors.
- Updated documentation and examples to illustrate the usage of overrides for both registered and unregistered steps.
- Added comprehensive tests to validate the new functionality and ensure backward compatibility.
* chore(normalization): addressing comments from copilot
* chore(learner): nit comment from copilot
* feat(pipeline): Enhance step_through method to support both tuple and dict inputs
* refactor(pipeline): Simplify observation and padding data handling in batch transitions
* Apply suggestions from code review
Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com>
Signed-off-by: Adil Zouitine <adilzouitinegm@gmail.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* refactor(pipeline): Introduce ComplementaryDataProcessor for handling complementary data in transitions
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* refactor(pipeline): Transition from tuple to dictionary format for EnvTransition
- Updated the EnvTransition structure to use a dictionary format instead of a tuple, enhancing readability and maintainability.
- Replaced instances of TransitionIndex with TransitionKey for accessing transition components.
- Adjusted related processing functions and tests to accommodate the new dictionary format, ensuring consistent handling of transitions across the codebase.
* refactor(observation_processor): Improve observation processing by using constants and simplifying pixel handling
- Introduced constants for observation keys to enhance readability.
- Streamlined the handling of the "pixels" key by copying observations first and processing images more clearly.
- Updated the environment state and agent position assignments to use the new constants, improving maintainability.
* feat(pipeline): Add hook unregistration functionality and enhance documentation
- Implemented methods to unregister before, after, and reset hooks in the RobotProcessor class, allowing for more flexible hook management.
- Enhanced documentation to clarify hook execution semantics and the implications of modifying transitions within hooks.
- Added comprehensive tests to verify the correct behavior of hook registration and unregistration, including error handling for non-existent hooks.
* refactor(pipeline): Clarify hook behavior and improve documentation
- Updated the RobotProcessor class to ensure hooks are strictly for observation and do not modify transitions, enhancing clarity and maintainability.
- Refactored hook registration methods to reflect the new behavior, ensuring they accept only functions that do not return modified transitions.
- Enhanced documentation to clearly outline the purpose of hooks and their execution semantics.
- Added tests to verify that hooks are not executed during the step_through method while ensuring they function correctly during the __call__ method.
* feat(pipeline): Add __repr__ method to RobotProcessor for improved readability
- Implemented a __repr__ method in the RobotProcessor class to provide a clear string representation of the processor, including step names and optional parameters like name and seed.
- Added comprehensive tests to validate the __repr__ output for various scenarios, including empty processors, single and multiple steps, custom names, and seed values.
- Ensured that the representation handles long lists of steps with truncation for better readability.
* chore(pipeline): Move _CFG_NAME along other class member
* refactor(pipeline): Utilize get_safe_torch_device for device assignment
- Replaced direct torch.device instantiation with get_safe_torch_device to ensure safe device handling.
- This change enhances code readability and maintains consistency in device management across the RobotProcessor class.
* refactor(pipeline): Enhance state filename generation and profiling method
- Updated state filename generation to use the registry name when available, improving clarity in saved files.
- Modified the profile_steps method to include a warmup_runs parameter, allowing for more controlled performance profiling.
- Ensured consistent conditions during profiling by deep copying transitions for each run, enhancing accuracy in timing results.
* chore(doc): address pip install commant lerobot that not exist yet
* feat(pipeline): Enhance configuration filename handling and state file naming
- Introduced support for custom configuration filenames in the `save_pretrained` method, allowing users to specify a filename instead of the default.
- Improved state file naming to include step indices, preventing conflicts when multiple processors of the same type are saved.
- Added automatic detection for configuration files when loading from a directory, with error handling for multiple files.
- Updated tests to validate new features, including custom filenames and automatic config detection.
* refactor(pipeline): Improve state file naming conventions for clarity and uniqueness
- Enhanced state file naming to include the processor's sanitized name, ensuring uniqueness when multiple processors are saved in the same directory.
- Updated tests to reflect changes in state file naming, verifying that filenames now include the processor name and step indices to prevent conflicts.
- Added a new test to validate state file naming when using multiple processors, ensuring distinct filenames for each processor's state files.
* docs(pipeline): Add clarification for repo name sanitization process
* feat(processors): Introduce processors for various policy types
- Added `make_processor` function to create processor instances for different policy types, including `tdmpc`, `diffusion`, `act`, `vqbet`, `pi0`, `pi0fast`, `sac`, and `reward_classifier`.
- Implemented corresponding processor files for each policy type, encapsulating normalization and unnormalization steps.
- Updated existing policies to remove direct normalization dependencies, enhancing modularity and clarity.
- Enhanced test coverage to validate the integration of new processors with existing policy configurations.
* refactor(learner): Remove normalization from cached image features retrieval
- Simplified the retrieval of observation features by removing the normalization step from the `get_cached_image_features` method calls.
- This change enhances clarity and aligns with the recent updates to policy processors.
* refactor(policies): Remove unnormalization step from action predictions
- Eliminated the unnormalization of actions in both `TDMPCPolicy` and `VQBeTPolicy` classes to streamline action prediction.
- This change improves code clarity and aligns with recent updates to policy processors.
* feat(train): Integrate preprocessor into training pipeline
* refactor(train): Update preprocessor initialization to include dataset statistics
* refactor(policies): Enhance processor creation and add NaN detection hook
* feat(record): Integrate RobotProcessor into recording loop and update policy handling
- Added support for RobotProcessor in the record_loop function to enhance data processing capabilities.
- Updated the logic to reset both policy and processor when provided, ensuring proper state management.
- Modified action prediction to utilize the processor, improving the overall functionality of the recording process.
- Adjusted the save_checkpoint function to include preprocessor state saving, enhancing checkpointing capabilities.
* feat(migration): Add script for migrating policy models with normalization layers
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* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* feat(migrate): Enhance migration script to create preprocessor and postprocessor for policy models
- Updated the migration script to generate both a preprocessor and a postprocessor, improving the handling of normalization for training and inference.
- Added functionality to convert features to PolicyFeature objects, ensuring compatibility with the new processor architecture.
- Refined the extraction and removal of normalization statistics and layers, streamlining the migration process.
- Improved error handling for missing mandatory configuration fields during model instantiation.
* feat(migrate): Add model card generation and saving to migration script
- Implemented functionality to generate and save a model card for the migrated model, including metadata such as dataset repository ID, license, and tags.
- Enhanced the script to push the model card to the hub if requested, improving model documentation and accessibility.
- Refactored the saving process to ensure the model card is saved locally and uploaded correctly when pushing to the hub.
* feat(processor): Introduce ToBatchProcessor for handling observation batching
- Added ToBatchProcessor to ensure observations have proper batch dimensions for model processing.
- Implemented functionality to add batch dimensions to state and image observations as needed.
- Created comprehensive unit tests to validate the processor's behavior with various tensor dimensions and types.
- Ensured compatibility with existing transition keys and maintained the integrity of non-observation data.
* feat(processors): Add ToBatchProcessor to multiple policy processors
- Integrated ToBatchProcessor into various policy processors to handle observation batching.
- Updated make functions for act, diffusion, pi0, pi0fast, sac, smolvla, tdmpc, and vqbet processors to include the new batching functionality.
- Ensured consistency across all processor implementations for improved data handling.
* refactor(factory): Remove unused imports and NaN detection hook from processor creation
* feat(batch_processor): Enhance ToBatchProcessor to handle action batching
- Updated ToBatchProcessor to add batch dimensions to actions in addition to observations.
- Implemented separate methods for processing observations and actions, improving code readability.
- Added comprehensive unit tests to validate action batching functionality across various tensor dimensions and types.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* feat(factory): Enhance make_processor to support preprocessor and postprocessor configuration
- Introduced ProcessorConfigKwargs TypedDict for better type safety in processor configuration.
- Updated make_processor to accept preprocessor and postprocessor configuration filenames, improving flexibility in processor instantiation.
- Refactored the loading of pretrained processors to utilize the new configuration options.
* refactor(factory): Clean up imports in factory.py
- Removed unused import of IdentityProcessor to streamline the code.
* feat(migrate): Extend load_model_from_hub to include train configuration
- Updated load_model_from_hub to return the train configuration alongside the model state_dict and config.
- Modified main function to handle the additional train configuration when loading models from both the hub and local paths.
- Adjusted dataset_repo_id extraction to utilize the train configuration for improved accuracy.
* refactor(record): Rename processor parameters and update processing logic
- Renamed `processor` to `preprocessor` and added `postprocessor` parameter for clarity.
- Updated the `record_loop` and `predict_action` functions to utilize the new preprocessor and postprocessor, enhancing the processing flow.
- Ensured compatibility with existing functionality while improving code readability.
* feat(batch_processor): Add task field processing to ToBatchProcessor
- Enhanced ToBatchProcessor to wrap string tasks in a list, adding batch dimensions for compatibility with model inference.
- Implemented a new method for processing complementary data, ensuring that task values are correctly handled as either strings or lists of strings.
- Added comprehensive unit tests to validate task processing, including edge cases and in-place mutation of complementary data.
* feat(normalization): Implement IDENTITY mode for normalization and unnormalization
- Enhanced NormalizerProcessor and UnnormalizerProcessor to support IDENTITY mode, allowing features to bypass normalization when specified.
- Updated processing logic to check normalization modes and handle missing statistics gracefully.
- Added comprehensive unit tests to validate IDENTITY mode functionality for both observations and actions, ensuring correct behavior across various scenarios.
- Improved error handling for unsupported normalization modes.
* fix(rebase): remove residual normalization layer:
* refactor(diffusion): remove normalization layer from input processing
* Add debug + calib
* cleanup
* Add pipeline
* fix int
* Add record example
* nit
* Add feature contract to pipelinestep and pipeline
* Add tests
* Add processor tests
* PR feedback
* encorperate pr feedback
* type in doc
* oops
* cleaned up steps and integrated pipeline with feature_contract
* refactor steps and robot to pipeline
* cleanup pipeline
* cleanup code further
* make it run
* feat(processors): Introduce processors for various policy types
- Added `make_processor` function to create processor instances for different policy types, including `tdmpc`, `diffusion`, `act`, `vqbet`, `pi0`, `pi0fast`, `sac`, and `reward_classifier`.
- Implemented corresponding processor files for each policy type, encapsulating normalization and unnormalization steps.
- Updated existing policies to remove direct normalization dependencies, enhancing modularity and clarity.
- Enhanced test coverage to validate the integration of new processors with existing policy configurations.
* refactor(learner): Remove normalization from cached image features retrieval
- Simplified the retrieval of observation features by removing the normalization step from the `get_cached_image_features` method calls.
- This change enhances clarity and aligns with the recent updates to policy processors.
* refactor(policies): Remove unnormalization step from action predictions
- Eliminated the unnormalization of actions in both `TDMPCPolicy` and `VQBeTPolicy` classes to streamline action prediction.
- This change improves code clarity and aligns with recent updates to policy processors.
* feat(train): Integrate preprocessor into training pipeline
* refactor(train): Update preprocessor initialization to include dataset statistics
* refactor(policies): Enhance processor creation and add NaN detection hook
* feat(record): Integrate RobotProcessor into recording loop and update policy handling
- Added support for RobotProcessor in the record_loop function to enhance data processing capabilities.
- Updated the logic to reset both policy and processor when provided, ensuring proper state management.
- Modified action prediction to utilize the processor, improving the overall functionality of the recording process.
- Adjusted the save_checkpoint function to include preprocessor state saving, enhancing checkpointing capabilities.
* feat(migration): Add script for migrating policy models with normalization layers
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* feat(migrate): Enhance migration script to create preprocessor and postprocessor for policy models
- Updated the migration script to generate both a preprocessor and a postprocessor, improving the handling of normalization for training and inference.
- Added functionality to convert features to PolicyFeature objects, ensuring compatibility with the new processor architecture.
- Refined the extraction and removal of normalization statistics and layers, streamlining the migration process.
- Improved error handling for missing mandatory configuration fields during model instantiation.
* feat(migrate): Add model card generation and saving to migration script
- Implemented functionality to generate and save a model card for the migrated model, including metadata such as dataset repository ID, license, and tags.
- Enhanced the script to push the model card to the hub if requested, improving model documentation and accessibility.
- Refactored the saving process to ensure the model card is saved locally and uploaded correctly when pushing to the hub.
* feat(processor): Introduce ToBatchProcessor for handling observation batching
- Added ToBatchProcessor to ensure observations have proper batch dimensions for model processing.
- Implemented functionality to add batch dimensions to state and image observations as needed.
- Created comprehensive unit tests to validate the processor's behavior with various tensor dimensions and types.
- Ensured compatibility with existing transition keys and maintained the integrity of non-observation data.
* feat(processors): Add ToBatchProcessor to multiple policy processors
- Integrated ToBatchProcessor into various policy processors to handle observation batching.
- Updated make functions for act, diffusion, pi0, pi0fast, sac, smolvla, tdmpc, and vqbet processors to include the new batching functionality.
- Ensured consistency across all processor implementations for improved data handling.
* refactor(factory): Remove unused imports and NaN detection hook from processor creation
* feat(batch_processor): Enhance ToBatchProcessor to handle action batching
- Updated ToBatchProcessor to add batch dimensions to actions in addition to observations.
- Implemented separate methods for processing observations and actions, improving code readability.
- Added comprehensive unit tests to validate action batching functionality across various tensor dimensions and types.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* feat(factory): Enhance make_processor to support preprocessor and postprocessor configuration
- Introduced ProcessorConfigKwargs TypedDict for better type safety in processor configuration.
- Updated make_processor to accept preprocessor and postprocessor configuration filenames, improving flexibility in processor instantiation.
- Refactored the loading of pretrained processors to utilize the new configuration options.
* refactor(factory): Clean up imports in factory.py
- Removed unused import of IdentityProcessor to streamline the code.
* feat(migrate): Extend load_model_from_hub to include train configuration
- Updated load_model_from_hub to return the train configuration alongside the model state_dict and config.
- Modified main function to handle the additional train configuration when loading models from both the hub and local paths.
- Adjusted dataset_repo_id extraction to utilize the train configuration for improved accuracy.
* refactor(record): Rename processor parameters and update processing logic
- Renamed `processor` to `preprocessor` and added `postprocessor` parameter for clarity.
- Updated the `record_loop` and `predict_action` functions to utilize the new preprocessor and postprocessor, enhancing the processing flow.
- Ensured compatibility with existing functionality while improving code readability.
* feat(batch_processor): Add task field processing to ToBatchProcessor
- Enhanced ToBatchProcessor to wrap string tasks in a list, adding batch dimensions for compatibility with model inference.
- Implemented a new method for processing complementary data, ensuring that task values are correctly handled as either strings or lists of strings.
- Added comprehensive unit tests to validate task processing, including edge cases and in-place mutation of complementary data.
* feat(normalization): Implement IDENTITY mode for normalization and unnormalization
- Enhanced NormalizerProcessor and UnnormalizerProcessor to support IDENTITY mode, allowing features to bypass normalization when specified.
- Updated processing logic to check normalization modes and handle missing statistics gracefully.
- Added comprehensive unit tests to validate IDENTITY mode functionality for both observations and actions, ensuring correct behavior across various scenarios.
- Improved error handling for unsupported normalization modes.
* fix(rebase): remove residual normalization layer:
* refactor(diffusion): remove normalization layer from input processing
* refactor(normalization): Remove unused state dict transformation methods and streamline imports
- Eliminated the _transform_state_dict_keys and _load_as_safetensor methods from PI0Policy, simplifying the model loading process.
- Cleaned up imports in modeling_pi0.py by removing log_model_loading_keys and init_logging.
- Updated TDMPCPolicy and VQBeTPolicy to handle action removal from batches during offline evaluation.
- Introduced hotswap_stats function in normalize_processor.py to update normalization statistics dynamically, with corresponding tests to ensure functionality.
* refactor(normalization): Clean up imports in normalize_processor.py
* feat(batch_processor): Add feature_contract method to ToBatchProcessor
- Introduced feature_contract method that returns features without modification, maintaining the no-op behavior of the processor.
- This addition enhances the flexibility of the ToBatchProcessor for future feature processing needs.
* fix(dependencies): Update transformers dependency constraint to allow only versions up to 4.52.0
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* feat(tokenizer): Introduce TokenizerProcessor for text tokenization
- Added TokenizerProcessor class to handle tokenization of task strings using Hugging Face's AutoTokenizer.
- Supports both string and list inputs, with customizable parameters for task key, output key, and tokenization settings.
- Implemented comprehensive unit tests to validate functionality, including handling of various input scenarios and integration with RobotProcessor.
- Updated types.py to include LANGUAGE feature type and modified __init__.py to register the new processor.
* feat(language): Enhance language processing in TokenizerProcessor
- Added OBS_LANGUAGE constant to define the observation language key.
- Updated TokenizerProcessor to store tokenized task data in the observation dictionary, ensuring compatibility with the new language feature.
- Introduced Pi0NewLineProcessor to append newlines to tasks for proper tokenization.
- Modified tests to validate the integration of language tokens and attention masks in the observation structure.
* feat(tokenizer): Add padding configuration to TokenizerProcessor
- Introduced `padding_side` parameter to the TokenizerProcessor for customizable padding direction.
- Updated the `make_pi0_processor` function to include the new padding configuration.
- Enhanced unit tests to validate the functionality of the `padding_side` parameter in various scenarios.
* feat(processor): Add state management methods to Pi0NewLineProcessor
* feat(normalization): Track normalization and unnormalization info in complementary data
- Updated NormalizerProcessor and UnnormalizerProcessor to accept additional parameters for tracking normalization modes.
- Enhanced the __call__ methods to store normalization and unnormalization information in the complementary data of transitions.
- Added unit tests to verify the correct tracking of normalization info, including scenarios with missing stats and selective normalization keys.
* feat(factory): Add preprocessor and postprocessor overrides to ProcessorConfigKwargs
- Updated ProcessorConfigKwargs to include optional overrides for preprocessor and postprocessor configurations.
- Enhanced the make_processor function to utilize the new overrides, allowing for more flexible processor initialization.
* feat(processors): Integrate RenameProcessor into various processor configurations
- Added RenameProcessor to the input steps of multiple processor functions, including make_act_processor, make_diffusion_processor, make_pi0_processor, make_sac_processor, make_tdmpc_processor, make_vqbet_processor, and make_smolvla_processor.
- Consolidated normalization features from input and output into a single NormalizerProcessor for improved efficiency.
- Updated the input steps to ensure compatibility with the new RenameProcessor integration.
* Do some todos and cleanup
* change feature_contract to dataset_features
* use one method for conversion pipeline output to add_frame dict and use base processors where possible
* Add back in and use record_loop
* update todo
* rename to_dataset_frame
* feat(smolvla): Refactor language processing and introduce new line processor (#1658)
- Removed the prepare_language method and directly accessed language tokens and masks from the batch using the OBS_LANGUAGE constant.
- Added SmolVLANewLineProcessor to ensure tasks end with a newline, enhancing tokenization compatibility.
- Updated the make_smolvla_processor function to include the new line processor and tokenizer processor for improved input handling.
* feat(processors): Integrate DeviceProcessor into multiple processor configurations
- Added DeviceProcessor to the input and output steps of various processor functions, including make_act_processor, make_diffusion_processor, make_pi0_processor, make_pi0fast_processor, make_sac_processor, make_tdmpc_processor, make_vqbet_processor, and make_smolvla_processor.
- Enhanced the DeviceProcessor class with state management methods and ensured compatibility with existing processor pipelines.
- Introduced unit tests for DeviceProcessor to validate functionality across different scenarios, including CPU and CUDA operations.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix
* fix reference frame
* refactor(pipeline): Remove to() method for device management
- Eliminated the to() method from RobotProcessor, which was responsible for moving tensor states to specified devices.
- Removed associated unit tests that validated the functionality of the to() method across various scenarios.
- Streamlined the pipeline code by focusing on other device management strategies.
* feat(processor): Enhance DeviceProcessor with float dtype conversion
- Added support for optional float dtype conversion in DeviceProcessor, allowing tensors to be converted to specified floating-point types while preserving non-float types.
- Implemented validation for float dtype input and updated the processor's configuration methods to include float dtype.
- Refactored tensor processing logic to streamline device movement and dtype conversion.
- Introduced comprehensive unit tests to validate the new float dtype functionality across various scenarios.
* update data visualization
* update teleop example
* fix record bugs
* Add replay
* Not code
* feature(pipeline): port tokenizer pipeline for VLA (#1645)
* feat(tokenizer): Introduce TokenizerProcessor for text tokenization
- Added TokenizerProcessor class to handle tokenization of task strings using Hugging Face's AutoTokenizer.
- Supports both string and list inputs, with customizable parameters for task key, output key, and tokenization settings.
- Implemented comprehensive unit tests to validate functionality, including handling of various input scenarios and integration with RobotProcessor.
- Updated types.py to include LANGUAGE feature type and modified __init__.py to register the new processor.
* feat(language): Enhance language processing in TokenizerProcessor
- Added OBS_LANGUAGE constant to define the observation language key.
- Updated TokenizerProcessor to store tokenized task data in the observation dictionary, ensuring compatibility with the new language feature.
- Introduced Pi0NewLineProcessor to append newlines to tasks for proper tokenization.
- Modified tests to validate the integration of language tokens and attention masks in the observation structure.
* feat(tokenizer): Add padding configuration to TokenizerProcessor
- Introduced `padding_side` parameter to the TokenizerProcessor for customizable padding direction.
- Updated the `make_pi0_processor` function to include the new padding configuration.
- Enhanced unit tests to validate the functionality of the `padding_side` parameter in various scenarios.
* feat(processor): Add state management methods to Pi0NewLineProcessor
* feat(normalization): Track normalization and unnormalization info in complementary data
- Updated NormalizerProcessor and UnnormalizerProcessor to accept additional parameters for tracking normalization modes.
- Enhanced the __call__ methods to store normalization and unnormalization information in the complementary data of transitions.
- Added unit tests to verify the correct tracking of normalization info, including scenarios with missing stats and selective normalization keys.
* feat(factory): Add preprocessor and postprocessor overrides to ProcessorConfigKwargs
- Updated ProcessorConfigKwargs to include optional overrides for preprocessor and postprocessor configurations.
- Enhanced the make_processor function to utilize the new overrides, allowing for more flexible processor initialization.
* feat(processors): Integrate RenameProcessor into various processor configurations
- Added RenameProcessor to the input steps of multiple processor functions, including make_act_processor, make_diffusion_processor, make_pi0_processor, make_sac_processor, make_tdmpc_processor, make_vqbet_processor, and make_smolvla_processor.
- Consolidated normalization features from input and output into a single NormalizerProcessor for improved efficiency.
- Updated the input steps to ensure compatibility with the new RenameProcessor integration.
* feat(smolvla): Refactor language processing and introduce new line processor (#1658)
- Removed the prepare_language method and directly accessed language tokens and masks from the batch using the OBS_LANGUAGE constant.
- Added SmolVLANewLineProcessor to ensure tasks end with a newline, enhancing tokenization compatibility.
- Updated the make_smolvla_processor function to include the new line processor and tokenizer processor for improved input handling.
* feture(policies): add device processor (#1659)
* feat(processors): Integrate DeviceProcessor into multiple processor configurations
- Added DeviceProcessor to the input and output steps of various processor functions, including make_act_processor, make_diffusion_processor, make_pi0_processor, make_pi0fast_processor, make_sac_processor, make_tdmpc_processor, make_vqbet_processor, and make_smolvla_processor.
- Enhanced the DeviceProcessor class with state management methods and ensured compatibility with existing processor pipelines.
- Introduced unit tests for DeviceProcessor to validate functionality across different scenarios, including CPU and CUDA operations.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* refactor(pipeline): Remove to() method for device management
- Eliminated the to() method from RobotProcessor, which was responsible for moving tensor states to specified devices.
- Removed associated unit tests that validated the functionality of the to() method across various scenarios.
- Streamlined the pipeline code by focusing on other device management strategies.
* feat(processor): Enhance DeviceProcessor with float dtype conversion
- Added support for optional float dtype conversion in DeviceProcessor, allowing tensors to be converted to specified floating-point types while preserving non-float types.
- Implemented validation for float dtype input and updated the processor's configuration methods to include float dtype.
- Refactored tensor processing logic to streamline device movement and dtype conversion.
- Introduced comprehensive unit tests to validate the new float dtype functionality across various scenarios.
* feat(policies): Add new line processors and update module exports
* feat(processor): Enhance batch and device processors to handle index and task_index fields
- Added logic to ToBatchProcessor for unsqueezing 0D tensors for index and task_index fields, ensuring they are processed as 1D tensors.
- Updated DeviceProcessor to process index and task_index fields in complementary data, preserving their tensor types and ensuring non-tensor fields remain unchanged.
- Enhanced unit tests to validate the correct handling of index and task_index fields across various scenarios, including device compatibility and dtype preservation.
* Add eval script
* fix `q_curr` in InverseKinematicsEEToJoints to the IK solution
* feat(processors): Introduce processors for various policy types
- Added `make_processor` function to create processor instances for different policy types, including `tdmpc`, `diffusion`, `act`, `vqbet`, `pi0`, `pi0fast`, `sac`, and `reward_classifier`.
- Implemented corresponding processor files for each policy type, encapsulating normalization and unnormalization steps.
- Updated existing policies to remove direct normalization dependencies, enhancing modularity and clarity.
- Enhanced test coverage to validate the integration of new processors with existing policy configurations.
* refactor(learner): Remove normalization from cached image features retrieval
- Simplified the retrieval of observation features by removing the normalization step from the `get_cached_image_features` method calls.
- This change enhances clarity and aligns with the recent updates to policy processors.
* refactor(policies): Remove unnormalization step from action predictions
- Eliminated the unnormalization of actions in both `TDMPCPolicy` and `VQBeTPolicy` classes to streamline action prediction.
- This change improves code clarity and aligns with recent updates to policy processors.
* feat(train): Integrate preprocessor into training pipeline
* refactor(train): Update preprocessor initialization to include dataset statistics
* refactor(policies): Enhance processor creation and add NaN detection hook
* feat(record): Integrate RobotProcessor into recording loop and update policy handling
- Added support for RobotProcessor in the record_loop function to enhance data processing capabilities.
- Updated the logic to reset both policy and processor when provided, ensuring proper state management.
- Modified action prediction to utilize the processor, improving the overall functionality of the recording process.
- Adjusted the save_checkpoint function to include preprocessor state saving, enhancing checkpointing capabilities.
* feat(migration): Add script for migrating policy models with normalization layers
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* feat(migrate): Enhance migration script to create preprocessor and postprocessor for policy models
- Updated the migration script to generate both a preprocessor and a postprocessor, improving the handling of normalization for training and inference.
- Added functionality to convert features to PolicyFeature objects, ensuring compatibility with the new processor architecture.
- Refined the extraction and removal of normalization statistics and layers, streamlining the migration process.
- Improved error handling for missing mandatory configuration fields during model instantiation.
* feat(migrate): Add model card generation and saving to migration script
- Implemented functionality to generate and save a model card for the migrated model, including metadata such as dataset repository ID, license, and tags.
- Enhanced the script to push the model card to the hub if requested, improving model documentation and accessibility.
- Refactored the saving process to ensure the model card is saved locally and uploaded correctly when pushing to the hub.
* feat(processor): Introduce ToBatchProcessor for handling observation batching
- Added ToBatchProcessor to ensure observations have proper batch dimensions for model processing.
- Implemented functionality to add batch dimensions to state and image observations as needed.
- Created comprehensive unit tests to validate the processor's behavior with various tensor dimensions and types.
- Ensured compatibility with existing transition keys and maintained the integrity of non-observation data.
* feat(processors): Add ToBatchProcessor to multiple policy processors
- Integrated ToBatchProcessor into various policy processors to handle observation batching.
- Updated make functions for act, diffusion, pi0, pi0fast, sac, smolvla, tdmpc, and vqbet processors to include the new batching functionality.
- Ensured consistency across all processor implementations for improved data handling.
* refactor(factory): Remove unused imports and NaN detection hook from processor creation
* feat(batch_processor): Enhance ToBatchProcessor to handle action batching
- Updated ToBatchProcessor to add batch dimensions to actions in addition to observations.
- Implemented separate methods for processing observations and actions, improving code readability.
- Added comprehensive unit tests to validate action batching functionality across various tensor dimensions and types.
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* feat(factory): Enhance make_processor to support preprocessor and postprocessor configuration
- Introduced ProcessorConfigKwargs TypedDict for better type safety in processor configuration.
- Updated make_processor to accept preprocessor and postprocessor configuration filenames, improving flexibility in processor instantiation.
- Refactored the loading of pretrained processors to utilize the new configuration options.
* refactor(factory): Clean up imports in factory.py
- Removed unused import of IdentityProcessor to streamline the code.
* feat(migrate): Extend load_model_from_hub to include train configuration
- Updated load_model_from_hub to return the train configuration alongside the model state_dict and config.
- Modified main function to handle the additional train configuration when loading models from both the hub and local paths.
- Adjusted dataset_repo_id extraction to utilize the train configuration for improved accuracy.
* refactor(record): Rename processor parameters and update processing logic
- Renamed `processor` to `preprocessor` and added `postprocessor` parameter for clarity.
- Updated the `record_loop` and `predict_action` functions to utilize the new preprocessor and postprocessor, enhancing the processing flow.
- Ensured compatibility with existing functionality while improving code readability.
* feat(batch_processor): Add task field processing to ToBatchProcessor
- Enhanced ToBatchProcessor to wrap string tasks in a list, adding batch dimensions for compatibility with model inference.
- Implemented a new method for processing complementary data, ensuring that task values are correctly handled as either strings or lists of strings.
- Added comprehensive unit tests to validate task processing, including edge cases and in-place mutation of complementary data.
* feat(normalization): Implement IDENTITY mode for normalization and unnormalization
- Enhanced NormalizerProcessor and UnnormalizerProcessor to support IDENTITY mode, allowing features to bypass normalization when specified.
- Updated processing logic to check normalization modes and handle missing statistics gracefully.
- Added comprehensive unit tests to validate IDENTITY mode functionality for both observations and actions, ensuring correct behavior across various scenarios.
- Improved error handling for unsupported normalization modes.
* fix(rebase): remove residual normalization layer:
* refactor(diffusion): remove normalization layer from input processing
* refactor(normalization): Remove unused state dict transformation methods and streamline imports
- Eliminated the _transform_state_dict_keys and _load_as_safetensor methods from PI0Policy, simplifying the model loading process.
- Cleaned up imports in modeling_pi0.py by removing log_model_loading_keys and init_logging.
- Updated TDMPCPolicy and VQBeTPolicy to handle action removal from batches during offline evaluation.
- Introduced hotswap_stats function in normalize_processor.py to update normalization statistics dynamically, with corresponding tests to ensure functionality.
* refactor(normalization): Clean up imports in normalize_processor.py
* feat(batch_processor): Add feature_contract method to ToBatchProcessor
- Introduced feature_contract method that returns features without modification, maintaining the no-op behavior of the processor.
- This addition enhances the flexibility of the ToBatchProcessor for future feature processing needs.
* fix(dependencies): Update transformers dependency constraint to allow only versions up to 4.52.0
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* feature(pipeline): port tokenizer pipeline for VLA (#1645)
* feat(tokenizer): Introduce TokenizerProcessor for text tokenization
- Added TokenizerProcessor class to handle tokenization of task strings using Hugging Face's AutoTokenizer.
- Supports both string and list inputs, with customizable parameters for task key, output key, and tokenization settings.
- Implemented comprehensive unit tests to validate functionality, including handling of various input scenarios and integration with RobotProcessor.
- Updated types.py to include LANGUAGE feature type and modified __init__.py to register the new processor.
* feat(language): Enhance language processing in TokenizerProcessor
- Added OBS_LANGUAGE constant to define the observation language key.
- Updated TokenizerProcessor to store tokenized task data in the observation dictionary, ensuring compatibility with the new language feature.
- Introduced Pi0NewLineProcessor to append newlines to tasks for proper tokenization.
- Modified tests to validate the integration of language tokens and attention masks in the observation structure.
* feat(tokenizer): Add padding configuration to TokenizerProcessor
- Introduced `padding_side` parameter to the TokenizerProcessor for customizable padding direction.
- Updated the `make_pi0_processor` function to include the new padding configuration.
- Enhanced unit tests to validate the functionality of the `padding_side` parameter in various scenarios.
* feat(processor): Add state management methods to Pi0NewLineProcessor
* feat(normalization): Track normalization and unnormalization info in complementary data
- Updated NormalizerProcessor and UnnormalizerProcessor to accept additional parameters for tracking normalization modes.
- Enhanced the __call__ methods to store normalization and unnormalization information in the complementary data of transitions.
- Added unit tests to verify the correct tracking of normalization info, including scenarios with missing stats and selective normalization keys.
* feat(factory): Add preprocessor and postprocessor overrides to ProcessorConfigKwargs
- Updated ProcessorConfigKwargs to include optional overrides for preprocessor and postprocessor configurations.
- Enhanced the make_processor function to utilize the new overrides, allowing for more flexible processor initialization.
* feat(processors): Integrate RenameProcessor into various processor configurations
- Added RenameProcessor to the input steps of multiple processor functions, including make_act_processor, make_diffusion_processor, make_pi0_processor, make_sac_processor, make_tdmpc_processor, make_vqbet_processor, and make_smolvla_processor.
- Consolidated normalization features from input and output into a single NormalizerProcessor for improved efficiency.
- Updated the input steps to ensure compatibility with the new RenameProcessor integration.
* feat(smolvla): Refactor language processing and introduce new line processor (#1658)
- Removed the prepare_language method and directly accessed language tokens and masks from the batch using the OBS_LANGUAGE constant.
- Added SmolVLANewLineProcessor to ensure tasks end with a newline, enhancing tokenization compatibility.
- Updated the make_smolvla_processor function to include the new line processor and tokenizer processor for improved input handling.
* feture(policies): add device processor (#1659)
* feat(processors): Integrate DeviceProcessor into multiple processor configurations
- Added DeviceProcessor to the input and output steps of various processor functions, including make_act_processor, make_diffusion_processor, make_pi0_processor, make_pi0fast_processor, make_sac_processor, make_tdmpc_processor, make_vqbet_processor, and make_smolvla_processor.
- Enhanced the DeviceProcessor class with state management methods and ensured compatibility with existing processor pipelines.
- Introduced unit tests for DeviceProcessor to validate functionality across different scenarios, including CPU and CUDA operations.
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* refactor(pipeline): Remove to() method for device management
- Eliminated the to() method from RobotProcessor, which was responsible for moving tensor states to specified devices.
- Removed associated unit tests that validated the functionality of the to() method across various scenarios.
- Streamlined the pipeline code by focusing on other device management strategies.
* feat(processor): Enhance DeviceProcessor with float dtype conversion
- Added support for optional float dtype conversion in DeviceProcessor, allowing tensors to be converted to specified floating-point types while preserving non-float types.
- Implemented validation for float dtype input and updated the processor's configuration methods to include float dtype.
- Refactored tensor processing logic to streamline device movement and dtype conversion.
- Introduced comprehensive unit tests to validate the new float dtype functionality across various scenarios.
* feat(policies): Add new line processors and update module exports
* feat(processor): Enhance batch and device processors to handle index and task_index fields
- Added logic to ToBatchProcessor for unsqueezing 0D tensors for index and task_index fields, ensuring they are processed as 1D tensors.
- Updated DeviceProcessor to process index and task_index fields in complementary data, preserving their tensor types and ensuring non-tensor fields remain unchanged.
- Enhanced unit tests to validate the correct handling of index and task_index fields across various scenarios, including device compatibility and dtype preservation.
* refactor(processors): Standardize processor naming conventions
- Updated processor names across various files to use a consistent "robot_preprocessor" and "robot_postprocessor" format.
- Modified the make_processor functions in factory, act, diffusion, pi0, pi0fast, sac, smolvla, tdmpc, and vqbet to reflect the new naming scheme.
- Enhanced the pipeline configuration to align with the updated processor names, improving clarity and maintainability.
* refactor(factory): Update processor configuration and type hints
- Changed return type of get_policy_class to type[PreTrainedPolicy] for improved type safety.
- Enhanced make_processor function to utilize dataset_stats in processor creation for better flexibility.
- Updated ProcessorConfigKwargs to include dataset_stats, allowing for more comprehensive processor configurations.
- Streamlined processor initialization by removing unnecessary kwargs and ensuring clarity in processor type handling.
* Fix eval and android gripper
* add some tests
* refactor(factory, pi0fast): Update processor function names and parameters
- Renamed make_pi0_processor to make_pi0fast_processor for clarity and consistency.
- Updated parameter names in the factory's make_processor function to use pretrained_model_name_or_path instead of source, enhancing readability and alignment with naming conventions.
* fix(train.py) push postprocessor with preprocessor
- Add preprocesser policy overrides for device and rename_map
- Add rename_map to DatasetRecordConfig (record.py)
* Cleanup pr
* fix more git diff pr issues
* add path as type in save_pretrained
* small nit
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* rename test file
* fix: make dataset_features/feature_contract is optional
* fix tests
* Encorperate pr feedback
* clean up record.py
* add ascii art, fix normal record
* remove merge issues
* fix merge
* remove features
* Add feedback PR
* fix last 4 tests
* remove features check
* rename to transform_features
* add transform_features
* fix lekiwi eval and update eval api example
---------
Signed-off-by: Adil Zouitine <adilzouitinegm@gmail.com>
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>
Co-authored-by: Adil Zouitine <adilzouitinegm@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com>
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>
* refactor(TokenizerProcessor): improve dependency handling and observation management
- Updated TokenizerProcessor to conditionally import AutoTokenizer based on the availability of the transformers library, enhancing flexibility.
- Modified tokenizer attribute type to Any to accommodate scenarios where transformers may not be installed.
- Improved observation handling by using a more concise approach to manage the transition dictionary, ensuring compatibility with existing data structures.
- Added error handling for missing transformers library, providing clear guidance for users on installation requirements.
* feat(dependencies): Add scipy as a required dependency
- Included `scipy>=1.15.2` in the project dependencies to enhance functionality and support for scientific computing tasks.
* feat(policies): convert save_policy_to_safetensors with pipeline
* refactor(normalization): remove Normalize and Unnormalize classes
- Deleted the Normalize and Unnormalize classes from the normalization module to streamline the codebase.
- Updated tests to ensure compatibility with the removal of these classes, focusing on the new NormalizerProcessor and UnnormalizerProcessor implementations.
- Enhanced the handling of normalization statistics and improved overall code clarity.
* refactor(factory): streamline processor loading by removing unused comments
- Removed commented-out code related to loading pretrained processors in the make_processor function.
- This change enhances code clarity and maintains focus on the current implementation.
* feat(DeviceProcessor): Enhance tensor processing with device detection and float dtype conversion
- Improved the _process_tensor method to preserve GPU placement for tensors already on a GPU, facilitating multi-GPU training scenarios.
- Introduced a new _detect_device method in TokenizerProcessor to ensure tokenized tensors match the device of existing tensors in transitions.
- Added comprehensive unit tests to validate the functionality of device detection and float dtype conversion across various scenarios.
* feat(tests): Add comprehensive tests for various policy processors
- Introduced new test files for ACT, Classifier, Diffusion, PI0, SAC, SmolVLA, TDMPC, and VQBeT policy processors.
- Each test file includes unit tests to validate functionality, including handling of batch sizes, device management, and data type conversions.
- Enhanced test coverage to ensure robustness and reliability of processor implementations across different scenarios.
* refactor(train): Remove unnecessary tensor device handling in training loop
* Refactor`gym_manipulator.py` using the universal pipeline (#1650)
* Migrate gym_manipulator to use the pipeline
Added get_teleop_events function to capture relevant events from teleop devices unrelated to actions
* Added the capability to record a dataset
* Added the replay functionality with the pipeline
* Refactored `actor.py` to use the pipeline
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* RL works at this commit - fixed actor.py and bugs in gym_manipulator
* change folder structure to reduce the size of gym_manip
* Refactored hilserl config
* Remove dataset and mode from HilSerlEnvConfig to a GymManipulatorConfig to reduce verbose of configs during training
* format docs
* removed get_teleop_events from abc
* Refactor environment configuration and processing pipeline for GymHIL support. Removed device attribute from HILSerlRobotEnvConfig, added DummyTeleopDevice for simulation, and updated processor creation to accommodate GymHIL environments.
* Improved typing for HILRobotEnv config and GymManipulator config
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* Migrated `gym_manipulator` to use a more modular structure similar to phone teleop
* Refactor gripper handling and transition processing in HIL and robot kinematic processors
- Updated gripper position handling to use a consistent key format across processors
- Improved the EEReferenceAndDelta class to handle reference joint positions.
- Added support for discrete gripper actions in the GripperVelocityToJoint processor.
- Refactored the gym manipulator to improve modularity and clarity in processing steps.
* Added delta_action_processor mapping wrapper
* Added missing file delta_action_processor and improved imports in `gym_manipulator`
* nit
* Added missing file joint_observation_processor
* Enhance processing architecture with new teleoperation processors
- Introduced `AddTeleopActionAsComplimentaryData` and `AddTeleopEventsAsInfo` for integrating teleoperator actions and events into transitions.
- Added `Torch2NumpyActionProcessor` and `Numpy2TorchActionProcessor` for seamless conversion between PyTorch tensors and NumPy arrays.
- Updated `__init__.py` to include new processors in module exports, improving modularity and clarity in the processing pipeline.
- GymHIL is now fully supported with HIL using the pipeline
* Refactor configuration structure for gym_hil integration
- Renamed sections for better readability, such as changing "Gym Wrappers Configuration" to "Processor Configuration."
- Enhanced documentation with clear examples for dataset collection and policy evaluation configurations.
* Enhance reset configuration and teleoperation event handling
- Added `terminate_on_success` parameter to `ResetConfig` and `InterventionActionProcessor` for controlling episode termination behavior upon success detection.
- Updated documentation to clarify the impact of `terminate_on_success` on data collection for reward classifier training.
- Refactored teleoperation event handling to use `TeleopEvents` constants for improved readability and maintainability across various modules.
* fix(keyboard teleop), delta action keys
* Added transform features and feature contract
* Added transform features for image crop
* Enum for TeleopEvents
* Update tranform_features delta action proc
---------
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* Remove HILEnvConfig references
* chore(processor): Add default names for preprocessor and postprocessor in constants
- Introduced `PREPROCESSOR_DEFAULT_NAME` and `POSTPROCESSOR_DEFAULT_NAME` constants for consistent naming across various processor implementations.
- Updated processor creation in multiple policy files to utilize these constants, enhancing code readability and maintainability.
- Modified the training script to load and save the preprocessor and postprocessor using the new constants.
* feat(processor): multiple improvements to the pipeline porting (#1749)
* [Port codebase pipeline] General fixes for RL and scripts (#1748)
* Refactor dataset configuration in documentation and codebase
- Updated dataset configuration keys from `dataset_root` to `root` and `num_episodes` to `num_episodes_to_record` for consistency.
- Adjusted replay episode handling by renaming `episode` to `replay_episode`.
- Enhanced documentation
- added specific processor to transform from policy actions to delta actions
* Added Robot action to tensor processor
Added new processor script for dealing with gym specific action processing
* removed RobotAction2Tensor processor; imrpoved choosing observations in actor
* nit in delta action
* added missing reset functions to kinematics
* Adapt teleoperate and replay to pipeline similar to record
* refactor(processors): move to inheritance (#1750)
* fix(teleoperator): improvements phone implementation (#1752)
* fix(teleoperator): protect shared state in phone implementation
* refactor(teleop): separate classes in phone
* fix: solve breaking changes (#1753)
* refactor(policies): multiple improvements (#1754)
* refactor(processor): simpler logic in device processor (#1755)
* refactor(processor): euclidean distance in delta action processor (#1757)
* refactor(processor): improvements to joint observations processor migration (#1758)
* refactor(processor): improvements to tokenizer migration (#1759)
* refactor(processor): improvements to tokenizer migration
* fix(tests): tokenizer tests regression from #1750
* fix(processors): fix float comparison and config in hil processors (#1760)
* chore(teleop): remove unnecessary callbacks in KeyboardEndEffectorTeleop (#1761)
* refactor(processor): improvements normalize pipeline migration (#1756)
* refactor(processor): several improvements normalize processor step
* refactor(processor): more improvements normalize processor
* refactor(processor): more changes to normalizer
* refactor(processor): take a different approach to DRY
* refactor(processor): final design
* chore(record): revert comment and continue deleted (#1764)
* refactor(examples): pipeline phone examples (#1769)
* refactor(examples): phone teleop + teleop script
* refactor(examples): phone replay + replay
* chore(examples): rename phone example files & folders
* feat(processor): fix improvements to the pipeline porting (#1796)
* refactor(processor): enhance tensor device handling in normalization process (#1795)
* refactor(tests): remove unsupported device detection test for complementary data (#1797)
* chore(tests): update ToBatchProcessor test (#1798)
* refactor(tests): remove in-place mutation tests for actions and complementary data in batch processor
* test(tests): add tests for action and task processing in batch processor
* add names for android and ios phone (#1799)
* use _tensor_stats in normalize processor (#1800)
* fix(normalize_processor): correct device reference for tensor epsilon handling (#1801)
* add point 5 add missing feature contracts (#1806)
* Fix PR comments 1452 (#1807)
* use key to determine image
* Address rest of PR comments
* use PolicyFeatures in transform_features
---------
Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>
---------
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>
Co-authored-by: Adil Zouitine <adilzouitinegm@gmail.com>
Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>
* refactor(constants, processor): standardize action and observation keys across multiple files (#1808)
- Added new constants for truncated and done states in constants.py.
- Updated references to action and observation keys in pipeline_features.py, converters.py, hil_processor.py, tokenizer_processor.py, and robot_kinematic_processor.py to use the new constants for improved readability and maintainability.
* refactor(processor): improve processor pipeline typing with generic type (#1810)
* refactor(processor): introduce generic type for to_output
- Always return `TOutput`
- Remove `_prepare_transition`, so `__call__` now always returns `TOutput`
- Update tests accordingly
- This refactor paves the way for adding settings for `to_transition` and `to_output` in `make_processor` and the post-processor
* refactor(processor): consolidate ProcessorKwargs usage across policies
- Removed the ProcessorTypes module and integrated ProcessorKwargs directly into the processor pipeline.
- Updated multiple policy files to utilize the new ProcessorKwargs structure for preprocessor and postprocessor arguments.
- Simplified the handling of processor kwargs by initializing them to empty dictionaries when not provided.
* refactor(converters): implement unified tensor conversion function (#1830)
- Introduced `to_tensor` function using `singledispatch` to handle various input types, including scalars, arrays, and dictionaries, converting them to PyTorch tensors.
- Replaced previous tensor conversion logic in `gym_action_processor`, `normalize_processor`, and `test_converters` with the new `to_tensor` function for improved readability and maintainability.
- Updated tests to cover new functionality and ensure correct tensor conversion behavior.
* Revert "refactor(converters): implement unified tensor conversion function (#…" (#1840)
This reverts commit a837685bf870919fc07ada287a71711cebabb1ea.
* refactor(converters): implement unified tensor conversion function (#1841)
- Introduced `to_tensor` function using `singledispatch` to handle various input types, including scalars, arrays, and dictionaries, converting them to PyTorch tensors.
- Replaced previous tensor conversion logic in `gym_action_processor`, `normalize_processor`, and `test_converters` with the new `to_tensor` function for improved readability and maintainability.
- Updated tests to cover new functionality and ensure correct tensor conversion behavior.
Co-authored-by: AdilZouitine <adilzouitinegm@gmail.com>
* refactor(converters): gather converters and refactor the logic (#1833)
* refactor(converters): move batch transition functions to converters module
- Moved `_default_batch_to_transition` and `_default_transition_to_batch` functions from `pipeline.py` to `converters.py` for better organization and separation of concerns.
- Updated references in `RobotProcessor` to use the new location of these functions.
- Added tests to ensure correct functionality of the transition functions, including handling of index and task_index fields.
- Removed redundant tests from `pipeline.py` to streamline the test suite.
* refactor(processor): reorganize EnvTransition and TransitionKey definitions
- Moved `EnvTransition` and `TransitionKey` classes from `pipeline.py` to a new `core.py` module for better structure and maintainability.
- Updated import statements across relevant modules to reflect the new location of these definitions, ensuring consistent access throughout the codebase.
* refactor(converters): rename and update dataset frame conversion functions
- Replaced `to_dataset_frame` with `transition_to_dataset_frame` for clarity and consistency in naming.
- Updated references in `record.py`, `pipeline.py`, and tests to use the new function name.
- Introduced `merge_transitions` to streamline the merging of transitions, enhancing readability and maintainability.
- Adjusted related tests to ensure correct functionality with the new naming conventions.
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* fix(processor): solve conflict artefacts
* refactor(converters): remove unused identity function and update type hints for merge_transitions
* refactor(processor): remove unused identity import and clean up gym_manipulator.py
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Steven Palma <steven.palma@huggingface.co>
* refactor(processors): add transform_features method to various processors (#1843)
* refactor(processors): update transition handling in RewardClassifierProcessor and InverseKinematicsEEToJoints (#1844)
* refactor(processors): unify import statements by consolidating pipeline imports into the main processor module (#1845)
* refactor(processors): add extended api for specialized pipelines (#1848)
* refactor(processors): enhance transform_features method across multiple processors (#1849)
* refactor(processors): enhance transform_features method across multiple processors
- Updated the transform_features method in various processors to utilize a copy of the features dictionary, ensuring immutability of the original features.
- Added handling for new feature keys and removed obsolete ones in the MapTensorToDeltaActionDict, JointVelocityProcessor, and others.
- Improved readability and maintainability by following consistent patterns in feature transformation.
* refactor(processors): standardize action and observation keys in delta_action_processor and joint_observations_processor
- Updated action and observation keys to use constants for improved readability and maintainability.
- Refactored the transform_features method in multiple processors to ensure consistent handling of feature keys.
- Enhanced error handling by raising exceptions for missing required components in action and observation processing.
- Removed obsolete code and improved overall structure for better clarity.
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* refactor(processors): remove unused import in joint_observations_processor
* refactor(processors): simplify transform_features method in delta_action_processor
* refactor(processors): streamline transform_features method in ImageCropResizeProcessor
* refactor(processors): improve error handling and streamline transform_features method in phone_processor
- Raised a ValueError for missing position and rotation in action to enhance error handling.
* refactor(processors): enhance error handling in JointVelocityProcessor
- Added a ValueError raise for missing current joint positions in the observation method to improve error handling and ensure the integrity of the transform_features method.
* refactor(processors): simplify transform_features method in robot kinematic processors
* refactor(processors): standardize action keys in phone_processor
* fix(processor): RKP feature obs -> act
---------
Signed-off-by: Adil Zouitine <adilzouitinegm@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Steven Palma <steven.palma@huggingface.co>
* chore(processor): rename RobotProcessor -> DataProcessorPipeline (#1850)
* chore(processor): rename specialized processor -> XYZProcessorStep (#1852)
* chore(processor): rename converters function names (#1853)
* chore(processor): rename to_transition_teleop_action -> action_to_transition
* chore(processor): rename to_transition_robot_observation -> observation_to_transition
* chore(processor): rename to_output_robot_action -> transition_to_robot_action
* chore(processor): add Step suffix to all processors (#1854)
* refactor(processor): rename MapDeltaActionToRobotAction and MapTensorToDeltaActionDict for consistency
* refactor(processor): rename DeviceProcessor to DeviceProcessorStep for consistency across modules
* refactor(processor): rename Torch2NumpyActionProcessor to Torch2NumpyActionProcessorStep for consistency
* refactor(processor): rename Numpy2TorchActionProcessor to Numpy2TorchActionProcessorStep for consistency
* refactor(processor): rename AddTeleopActionAsComplimentaryData to AddTeleopActionAsComplimentaryDataStep for consistency
* refactor(processor): rename ImageCropResizeProcessor and AddTeleopEventsAsInfo for consistency
* refactor(processor): rename TimeLimitProcessor to TimeLimitProcessorStep for consistency
* refactor(processor): rename GripperPenaltyProcessor to GripperPenaltyProcessorStep for consistency
* refactor(processor): rename InterventionActionProcessor to InterventionActionProcessorStep for consistency
* refactor(processor): rename RewardClassifierProcessor to RewardClassifierProcessorStep for consistency
* refactor(processor): rename JointVelocityProcessor to JointVelocityProcessorStep for consistency
* refactor(processor): rename MotorCurrentProcessor to MotorCurrentProcessorStep for consistency
* refactor(processor): rename NormalizerProcessor and UnnormalizerProcessor to NormalizerProcessorStep and UnnormalizerProcessorStep for consistency
* refactor(processor): rename VanillaObservationProcessor to VanillaObservationProcessorStep for consistency
* refactor(processor): rename RenameProcessor to RenameProcessorStep for consistency
* refactor(processor): rename TokenizerProcessor to TokenizerProcessorStep for consistency
* refactor(processor): rename ToBatchProcessor to AddBatchDimensionProcessorStep for consistency
* refactor(processor): update config file name in test for RenameProcessorStep consistency
* refactor(processor): rename internal tokenizer variable for clarity (#1855)
- Changed the internal tokenizer variable name from `_tokenizer` to `input_tokenizer` for improved readability and consistency.
- Updated references throughout the class to reflect the new variable name.
* chore(processor): rename merge_features -> combine_feature_dicts (#1856)
* refactor(processor): rename internal device variable for clarity (#1857)
- Changed the internal device variable from `_device` to `tensor_device` for improved readability and consistency.
- Updated references throughout the class to reflect the new variable name.
* chore(processor): rename teleop_phone variable names (#1858)
* chore(processor): add type alias RobotProcessorPipeline and PolicyProcessorPipeline (#1859)
* feat(processor): introduce PolicyProcessorPipeline and RobotProcessorPipeline as type aliases for DataProcessorPipeline
- Added PolicyProcessorPipeline and RobotProcessorPipeline type aliases to enhance clarity and maintainability in the processor module.
- Updated the __all__ list to include the new pipelines for better module export consistency.
* refactor(processor): replace DataProcessorPipeline with PolicyProcessorPipeline across multiple modules
- Updated all instances of DataProcessorPipeline to PolicyProcessorPipeline in various processor files for consistency and clarity.
- Adjusted function signatures to reflect the new pipeline type, enhancing maintainability and readability.
* refactor(processor): update hotswap_stats function to use PolicyProcessorPipeline
- Changed the parameter name from robot_processor to policy_processor for clarity.
- Ensured consistency with recent updates to the processor module by reflecting the new pipeline type in the function signature.
* refactor(processor): replace DataProcessorPipeline with PolicyProcessorPipeline in migrate_policy_normalization.py
- Updated the preprocessor and postprocessor to use PolicyProcessorPipeline for consistency with recent changes in the processor module.
- Enhanced clarity and maintainability by aligning with the new pipeline structure.
* refactor(processor): update hotswap_stats to use PolicyProcessorPipeline
- Changed the parameter type in hotswap_stats from DataProcessorPipeline to PolicyProcessorPipeline for consistency with recent updates.
- Enhanced clarity by updating the function documentation to reflect the new pipeline type.
* refactor(processor): replace DataProcessorPipeline with RobotProcessorPipeline across multiple files
- Updated instances of DataProcessorPipeline to RobotProcessorPipeline in evaluate.py, record.py, replay.py, teleoperate.py, and other relevant files for consistency and clarity.
- Adjusted function signatures and variable types to reflect the new pipeline structure, enhancing maintainability and readability.
* refactor(processor): enforce config_filename requirement for HF Hub loading (#1860)
- Updated the DataProcessorPipeline to require a specific config_filename when loading from Hugging Face Hub, enhancing clarity and preventing errors.
- Simplified local path checks and improved error handling for invalid paths.
- Adjusted tests to reflect the new requirement and ensure proper error handling for various loading scenarios.
* feat(record): add transition features to dataset and handle scalar vs array formatting in converters (#1861)
- Introduced new transition features (`next.reward`, `next.done`, `next.truncated`) in the dataset during recording.
- Updated the `transition_to_dataset_frame` function to handle scalar values correctly, ensuring compatibility with expected array formats for reward, done, and truncated features.
* refactor(pipeline): enforce ProcessorStep inheritance for pipeline steps (#1862)
- Updated the DataProcessorPipeline to require that all steps inherit from ProcessorStep, enhancing type safety and clarity.
- Adjusted tests to utilize a MockTokenizerProcessorStep that adheres to the ProcessorStep interface, ensuring consistent behavior across tests.
- Refactored various mock step classes in tests to inherit from ProcessorStep for improved consistency and maintainability.
* refactor(dependencies): remove scipy dependency and introduce custom rotation utilities (#1863)
- Removed the scipy dependency from the project to streamline requirements.
- Added a new `rotation.py` module containing a custom `Rotation` class that replicates essential functionalities of `scipy.spatial.transform.Rotation`, allowing for rotation vector, matrix, and quaternion conversions without external dependencies.
- Updated the `robot_kinematic_processor.py` to utilize the new custom rotation utilities.
* feat(teleoperation): introduce HasTeleopEvents protocol and enhance teleop event handling (#1866)
- Added the HasTeleopEvents protocol to define a standard for teleoperators that provide control events.
- Implemented a runtime check to ensure teleoperators implement the get_teleop_events() method.
- Updated AddTeleopEventsAsInfoStep to utilize the new protocol, enhancing compatibility with custom teleoperators.
- Improved documentation for clarity on teleoperation event extraction and compatibility with built-in teleoperators.
* fix(deps): use in-house rotation utils over scipy throughout the codebase
* refactor(constants): rename preprocessor and postprocessor constants for clarity (#1868)
- Updated constant names from PREPROCESSOR_DEFAULT_NAME and POSTPROCESSOR_DEFAULT_NAME to POLICY_PREPROCESSOR_DEFAULT_NAME and POLICY_POSTPROCESSOR_DEFAULT_NAME for better context.
- Adjusted references across multiple files to use the new constant names, ensuring consistency in the codebase.
* refactor(tests): update processor test assertions to reflect new preprocessor and postprocessor names (#1869)
- Changed assertions in multiple processor test files to verify the updated names from "robot_preprocessor" and "robot_postprocessor" to "policy_preprocessor" and "policy_postprocessor" for consistency with recent refactoring.
* refactor(utils): simplify log_rerun_data function (#1864)
* refactor(logging): enhance log_rerun_data to handle observation and action separately
- Updated the `log_rerun_data` function to accept and log observation and action data more clearly, improving readability and maintainability.
- Refactored the `record_loop` and `teleop_loop` functions to extract and pass observation and action data to `log_rerun_data`, ensuring consistent logging format.
* refactor(tests): update test_log_rerun_data to align with log_rerun_data changes
- Modified test cases in `test_visualization_utils.py` to extract and pass observation and action data separately to `log_rerun_data`, improving clarity and consistency with recent function updates.
- Ensured that the tests reflect the new structure of `log_rerun_data` for better maintainability.
* refactor(processors): simplify calls to log_rerun + replace lambda functions with identity_transition
---------
Co-authored-by: Steven Palma <steven.palma@huggingface.co>
* fix(processor): recover type inference for use of processors (#1873)
* refactor(processors): Improve Normalization Processor Performance and Device/Dtype Adaptability (#1880)
* refactor(processors): reorder processor steps for consistency across implementations
- Updated the order of processor steps in multiple files to ensure consistency, placing AddBatchDimensionProcessorStep and DeviceProcessorStep before NormalizerProcessorStep.
- Adjusted related test assertions to reflect the new order of steps in the preprocessor, enhancing clarity and maintainability.
* refactor(normalization): remove dtype specification in tensor conversion for adaptation logic
- Updated tensor conversion in the _NormalizationMixin class to remove explicit dtype specification, allowing for automatic adaptation of tensor types.
- Adjusted related tests to ensure proper functionality with the new tensor conversion logic, verifying that normalizers adapt correctly to input types.
* chore(docs): update doctrines pipeline files (#1872)
* docs(processor): update docstrings batch_processor
* docs(processor): update docstrings device_processor
* docs(processor): update docstrings tokenizer_processor
* update docstrings processor_act
* update docstrings for pipeline_features
* update docstrings for utils
* update docstring for processor_diffusion
* update docstrings factory
* add docstrings to pi0 processor
* add docstring to pi0fast processor
* add docstring classifier processor
* add docstring to sac processor
* add docstring smolvla processor
* add docstring to tdmpc processor
* add docstring to vqbet processor
* add docstrings to converters
* add docstrings for delta_action_processor
* add docstring to gym action processor
* update hil processor
* add docstring to joint obs processor
* add docstring to migrate_normalize_processor
* update docstrings normalize processor
* update docstring normalize processor
* update docstrings observation processor
* update docstrings rename_processor
* add docstrings robot_kinematic_processor
* cleanup rl comments
* add docstring to train.py
* add docstring to teleoperate.py
* add docstrings to phone_processor.py
* add docstrings to teleop_phone.py
* add docstrings to control_utils.py
* add docstrings to visualization_utils.py
---------
Co-authored-by: Pepijn <pepijn@huggingface.co>
* refactor(eval): integrate preprocessor and postprocessor into rollout and eval_policy functions (#1900)
* refactor(eval): integrate preprocessor and postprocessor into rollout and eval_policy functions
- Updated the `rollout` and `eval_policy` functions to accept preprocessor and postprocessor parameters, enhancing the flexibility of the evaluation pipeline.
- Adjusted the implementation to apply preprocessing and postprocessing steps during policy evaluation, improving the overall data handling and processing flow.
* refactor(eval): remove redundant observation device conversion in rollout function
- Eliminated unnecessary device conversion for the observation dictionary within the `rollout` function, streamlining the code and enhancing readability.
- This change simplifies the observation handling process, aligning with the preference for clearer solutions.
* debug
* refactor(utils): enhance task handling in add_envs_task function
- Improved the `add_envs_task` function to validate the output of `task_description` and `task` calls, ensuring they return lists of strings.
- Removed the use of `else` statement for environments without language instructions, simplifying the logic and enhancing readability.
- Streamlined the observation dictionary handling by ensuring consistent data types for task attributes.
* refactor(converters): rename _from_tensor to from_tensor_to_numpy for clarity (#1902)
- Updated the function name from _from_tensor to from_tensor_to_numpy to better reflect its purpose of converting PyTorch tensors to numpy arrays or scalars.
- Adjusted all references to the renamed function throughout the codebase to maintain consistency.
- Enhanced the _NormalizationMixin class to reconstruct the stats dictionary from tensor stats using the new function, ensuring compatibility after loading state dicts.
- Added tests to verify the correct reconstruction of stats and functionality of methods dependent on self.stats after loading.
* refactor(pipeline): feature contract now categorizes between OBS or Action (#1867)
* refactor(processor): signature of transform_features
* refactor(processor): remove prefixes + processor respect new transform_features signature + update test accordingly
* refactor(processor): rename now is only for visual
* refactor(processor): update normalize processor
* refactor(processor): update vanilla processor features
* refactor(processor): feature contract now uses its own enum
* chore(processor): rename renameprocessor
* chore(processor): minor changes
* refactor(processor): add create & change aggregate
* refactor(processor): update aggregate
* refactor(processor): simplify to functions, fix features contracts and rename function
* test(processor): remove to converter tests as now they are very simple
* chore(docs): recover docs joint observations processor
* fix(processor): update RKP
* fix(tests): recv diff test_pipeline
* chore(tests): add docs to test
* chore(processor): leave obs language constant untouched
* fix(processor): correct new shape of feature in crop image processor
* refactor(eval): specify type parameters for preprocessor and postprocessor in eval_policy function (#1904)
* chore(processor): remove action prefixes (#1905)
* test(processor): all processors use now the same create_transition (#1906)
* test(processor): all processors use now the same create_transition
* test(processor): use identity instead of lambda for transition in pipelines
* fix(processor): specialized processors respect contract by raising if none (#1909)
* fix(processor): specialized processor now raise
* test(processor): fix tests for now raise specialized processors
* test(processor): use identity in newly introduced pipeline
* refactor(processor): clarify action types, distinguish PolicyAction, RobotAction, and EnvAction (#1908)
* refactor(processor): split action from policy, robots and environment
- Updated function names to robot_action_to_transition and robot_transition_to_action across multiple files to better reflect their purpose in processing robot actions.
- Adjusted references in the RobotProcessorPipeline and related components to ensure compatibility with the new naming convention.
- Enhanced type annotations for action parameters to improve code readability and maintainability.
* refactor(converters): rename robot_transition_to_action to transition_to_robot_action
- Updated function names across multiple files to improve clarity and consistency in processing robot actions.
- Adjusted references in RobotProcessorPipeline and related components to align with the new naming convention.
- Simplified action handling in the AddBatchDimensionProcessorStep by removing unnecessary checks for action presence.
* refactor(converters): update references to transition_to_robot_action
- Renamed all instances of robot_transition_to_action to transition_to_robot_action across multiple files for consistency and clarity in the processing of robot actions.
- Adjusted the RobotProcessorPipeline configurations to reflect the new naming convention, enhancing code readability.
* refactor(processor): update Torch2NumpyActionProcessorStep to extend ActionProcessorStep
- Changed the base class of Torch2NumpyActionProcessorStep from PolicyActionProcessorStep to ActionProcessorStep, aligning it with the current architecture of action processing.
- This modification enhances the clarity of the class's role in the processing pipeline.
* fix(processor): main action processor can take also EnvAction
---------
Co-authored-by: Steven Palma <steven.palma@huggingface.co>
* refactor(processor): phone processor is now an RobotActionProcessorStep
* fix(processor): use subprocessors in AddBatchDimensionProcessorStep only if we have the ingredients
* fix(robots): remove action prefix hard-coded in teleop keyboard and gamepad
* feat(processor): enhance type safety with generic DataProcessorPipeline for policy and robot pipelines (#1915)
* refactor(processor): enhance type annotations for processors in record, replay, teleoperate, and control utils
- Updated type annotations for preprocessor and postprocessor parameters in record_loop and predict_action functions to specify the expected dictionary types.
- Adjusted robot_action_processor type in ReplayConfig and TeleoperateConfig to improve clarity and maintainability.
- Ensured consistency in type definitions across multiple files, enhancing overall code readability.
* refactor(processor): enhance type annotations for RobotProcessorPipeline in various files
- Updated type annotations for RobotProcessorPipeline instances in evaluate.py, record.py, replay.py, teleoperate.py, and other related files to specify input and output types more clearly.
- Introduced new type conversions for PolicyAction and EnvTransition to improve type safety and maintainability across the processing pipelines.
- Ensured consistency in type definitions, enhancing overall code readability and reducing potential runtime errors.
* refactor(processor): update transition handling in processors to use transition_to_batch
- Replaced direct transition handling with transition_to_batch in various processor tests and implementations to ensure consistent batching of input data.
- Updated assertions in tests to reflect changes in data structure, enhancing clarity and maintainability.
- Improved overall code readability by standardizing the way transitions are processed across different processor types.
* refactor(tests): standardize transition key usage in processor tests
- Updated assertions in processor test files to utilize the TransitionKey for action references, enhancing consistency across tests.
- Replaced direct string references with TransitionKey constants for improved readability and maintainability.
- Ensured that all relevant tests reflect these changes, contributing to a more uniform approach in handling transitions.
* refactor(processor): unify action imports and enhance type clarity across multiple files
- Updated imports in various files to include RobotAction and PolicyAction directly from the processor module, improving clarity and consistency.
- Removed redundant imports from core, streamlining the codebase and enhancing maintainability.
- Adjusted type annotations and references in the RobotProcessorPipeline and related components to align with the new import structure, ensuring better type safety and readability.
* refactor(processor): migrate policy normalization to use factory functions
- Updated the migration script to utilize `make_pre_post_processors` and `make_policy_config` from `lerobot.policies.factory`, enhancing consistency with the current codebase.
- Improved normalization statistics extraction and processor pipeline creation, ensuring compatibility with the new `PolicyProcessorPipeline` architecture.
- Cleaned up configuration handling by removing unnecessary fields and adding normalization mapping directly to the config.
- Enhanced type safety and readability by refining feature type and normalization mode handling.
* debug(scripts): simplify record with processors (#1918)
Co-authored-by: Adil Zouitine <adilzouitinegm@gmail.com>
* refactor(processor): update migration script for policy normalization and hub integration
- Modified the migration script to include a branch argument for pushing to the hub, enhancing flexibility in version control.
- Improved error handling by ensuring the policy type is extracted from the configuration, promoting robustness.
- Streamlined the process of saving and pushing model components to the hub, allowing for a single commit with optional PR creation.
- Updated the commit message and description for better clarity on the migration changes and benefits, ensuring users are informed of the new architecture and usage.
* fixes for processors used in phone teleop
* fixes for rotation matrix
* add empty obs and act in create_initial_features
* use observation instead of obs
* docs(processor): update docstrings pipeline (#1920)
* chore(docs): Processor doc (#1685)
* chore(docs): initialize doc
* Added script for the second part of the processor doc
* precommit style nit
* improved part 2 of processor guide
* Add comprehensive documentation for processors in robotics
- Introduced a detailed guide on processors, covering their role in transforming raw robot data into model-ready inputs and vice versa.
- Explained core concepts such as EnvTransition, ProcessorStep, and RobotProcessor, along with their functionalities.
- Included examples of common processor steps like normalization, device management, batch processing, and text tokenization.
- Provided insights on building complete pipelines, integrating processors into training loops, and saving/loading configurations.
- Emphasized best practices and advanced features for effective usage of processors in robotics applications.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* feat(docs): Enhance introduction to processors with additional converter functions
- Updated the introduction to processors documentation to include default batch-to-transition and transition-to-batch converters.
- Added detailed descriptions and examples for new specialized converter functions: `to_transition_teleop_action`, `to_transition_robot_observation`, `to_output_robot_action`, and `to_dataset_frame`.
- Improved clarity on how these converters facilitate integration with existing robotics applications.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Improved doc implement_your_own_pipeline
- Use normalization processor as default example
- Add section on transform features
- Add section on overrides.
* Add phone docs and use pipeline for robots/teleop docs
* Fix typo in documentation for adapters in robots/teleop section
* Enhance documentation for processors with detailed explanations and examples
- Updated the introduction to processors, clarifying the role of `EnvTransition` and `ProcessorStep`.
- Introduced `DataProcessorPipeline` as a generic orchestrator for chaining processor steps.
- Added comprehensive descriptions of new converter functions and their applications.
- Improved clarity on type safety and the differences between `RobotProcessorPipeline` and `PolicyProcessorPipeline`.
- Included examples for various processing scenarios, emphasizing best practices for data handling in robotics.
* Enhance documentation for processor migration and debugging
- Added detailed sections on the migration of models to the new `PolicyProcessorPipeline` system, including breaking changes and migration scripts.
- Introduced a comprehensive guide for debugging processor pipelines, covering common issues, step-by-step inspection, and runtime monitoring techniques.
- Updated examples to reflect new usage patterns and best practices for processor implementation and error handling.
- Clarified the role of various processor steps and their configurations in the context of robotics applications.
---------
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Pepijn <pepijn@huggingface.co>
* docs: Add new section for debugging processor pipelines
- Introduced a new documentation entry for debugging processor pipelines, enhancing the existing guide on processors.
- This addition aims to provide users with insights and best practices for troubleshooting and optimizing their processor workflows.
* fix(processor): phone examples (#1921)
* fix(processor): phone examples
* chore(processor): simplify gripper in phone example kinematic chain
---------
Co-authored-by: Steven Palma <steven.palma@huggingface.co>
* refactor(processors): several additions (#1926)
* chore(processor): remove merge_transitions functions (#1925)
* refactor(processors): move processors out of configs (#1927)
* chore(processor): streamline combine_features_dict (#1928)
* chore(policies): use new constants (#1929)
* fix(deps): right version transformers (#1930)
* fix(tests): add none + disable async tests for now (#1931)
* refactor(processor): transform_features loop + EAFP (#1932)
* fix(processors): make sure nested dict are also shallow copied (#1939)
* refactor(processor): replace ModelHubMixin with HubMixin and enhance save_pretrained method (#1937)
- Updated DataProcessorPipeline to use HubMixin instead of ModelHubMixin for improved functionality.
- Refactored save_pretrained method to handle saving
* refactor(docs): streamline monitoring hooks and enhance performance reporting
- Removed the log_shapes and measure_performance hooks, simplifying the monitoring process to focus on NaN checks.
- Updated performance reporting to include maximum processing times alongside average times for better insights.
- Clarified documentation regarding the processing pipeline and feature transformations.
* fix teleop, record and eval (#1940)
* fix cmd record, eval
* chore(processor): update input output of main 3 processors for better semantics (#1942)
* chore(processor): update input output of main 3 processors for better semantics
* refactor(processor): replace Any with RobotObservation for improved type safety in processors
* fix(processors): no PolicyObservation
* chore(processor): update with RobotObservation
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: AdilZouitine <adilzouitinegm@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* test(processor): fix batch expectation
* feat(example): Add SO100 EE pipeline control (teleop+record) (#1943)
* feat(examples): add ee so100 processors teleop & record
* refactor(processor): improve FK processor for better use compatability
* docs(processor): enhance tutorial on implementing custom processors
- Updated the tutorial to use `NormalizerProcessorStep` as the primary example, clarifying its role in normalizing observations and actions.
- Improved explanations of the need for custom processors, emphasizing data compatibility and processing requirements.
- Added code snippets demonstrating the normalization process and the configuration of processor pipelines.
- Enhanced the introduction to processors, detailing their function as translators between raw robot data and model inputs.
- Included examples of real-world processor configurations for both training and inference scenarios.
* docs(debug): enhance debugging guide for processor pipelines
- Streamlined the introduction to clarify the challenges of debugging complex processor pipelines.
- Expanded the section on hooks, detailing their purpose and implementation for runtime monitoring.
- Introduced step-by-step debugging techniques, emphasizing the use of the `step_through()` method for inspecting intermediate states.
- Added examples of feature validation to ensure data structure contracts are met.
- Consolidated best practices for debugging, highlighting the synergy between hooks, step-through debugging, and feature validation.
* chore(processors): tokenizers raises and remove tensor conversion (#1949)
* chore(processor): remove unused transition_features dict
* feat(ee): add so100_to_so100_EE replay and evaluate examples
* chore(examples): homogenize style across example files (#1955)
* chore(examples): homogenize style across example files
* chore(examples): homogenize style across example files eval + replay
* chore(examples): homogenize headers
* test(async): fix feature manipulation (#1957)
* test(async): fix feature manipulation
* chore(processor): remove unused functions
* fix(processor): Preserve stats overrides in normalizer load_state_dict and fix training resumption (#1958)
* feat(processor): enhance normalization handling and state management
- Added support for additional normalization modes including IDENTITY.
- Introduced a new function `clean_state_dict` to remove specific substrings from state dict keys.
- Implemented preservation of explicitly provided normalization statistics during state loading.
- Updated training script to conditionally provide dataset statistics based on resume state.
- Expanded tests to verify the correct behavior of stats override preservation and loading.
* fix(train): remove redundant comment regarding state loading
- Removed a comment that noted the preprocessor and postprocessor state is already loaded when resuming training, as it was deemed unnecessary for clarity.
* test(processor): update tests to handle missing or invalid task keys
- Modified tests to assert that the processor raises appropriate exceptions when the task key is missing or has an invalid value in the complementary data.
- Ensured that the tests cover cases for None, integer, and mixed list task values, improving robustness against invalid inputs.
* fix(processor): enforce signatures
* chore(processor): update comments in record.py
* test(processor): fix isinstance and cuda test
* modify phone docs
* fix(processor): reorder output steps to ensure correct processing sequence (#1961)
- Moved DeviceProcessorStep to the end of the output steps in multiple processor files to maintain the intended processing order.
- Updated corresponding tests to reflect the change in step order.
* fix(processors): assumptions for robot_action_processor & teleop_action_processor (#1964)
* fix(processors): new assumptions pipeline
* fix(processors): ee jj phone teleop replay record working
* chore(processors): update comments and default vars
* chore(processor): remove unnecessary copy
* chore(processor): added todo assumption gripper
* fix(processors): eval using detected device
* finish phone docs
* fix correct image link
* feat(processor): implement migration detection and error handling for processor configurations (#1968)
* feat(processor): implement migration detection and error handling for processor configurations
- Added ProcessorMigrationError to handle migration requirements for old model formats.
- Enhanced DataProcessorPipeline.from_pretrained to include robust migration detection logic.
- Implemented methods for resolving configuration sources, validating loaded configs, and checking for valid processor configurations.
- Introduced comprehensive tests for migration detection and configuration validation to ensure correct behavior.
* refactor(processor): simplify loading logic and enhance migration detection
- Refactored DataProcessorPipeline to implement a simplified three-way loading strategy for configuration files.
- Introduced explicit config_filename parameter to avoid ambiguity during loading.
- Updated ProcessorMigrationError to provide clearer error messages for migration requirements.
- Enhanced tests to cover new loading logic and ensure proper migration detection.
- Removed deprecated methods related to config source resolution.
* fix(processor) RL (#1953)
* fix(gym_manipulator) general fixes to make it compitable
* fix for dataset v3.0
* fix for gym_manipulator
* add map policy action to robot action wrappers in a seperate scripts
* added unittest for policy to robot bridge
* fixes for gripper penalty
* fix style
* fix gamepad controller
* fixes for sim teleop
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* modify numpy2torch to a regular processor as a quick fix
* missing imports?!
* - Removed the use of `AddRobotObservationAsComplimentaryData` from `gym_manipulator` and thus the codebase
- Added get_raw_joint_positions functions to RobotEnv
- Pass raw_joint_positions as input to the action_pipeline in `gym_manipulator`
- Add `InverseKinematicsRLStep` to be tailored towards the need of RL which requires the use of the IK solution as the main reference point of the control loop
- Added the option `use_ik_solution` in `EEReferenceDelta` step to rely on the ik solution rather than the joint values
* -Updated links to all the config files to place them in the new repo with configs compatible with the pipeline
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* fix(tests): update test cases for loading pipelines with specific config filenames
- Modified test cases to include explicit configuration filenames when loading pipelines in `test_policy_robot_bridge.py`.
- Ensured that the tests reflect the correct loading behavior for both robot-to-policy and policy-to-robot transitions.
* fix(examples): train mps processor (#1970)
* fix(examples): train mps processor
* fix(processor): add MPS compatibility for float64 tensors
- Implemented a workaround to convert float64 tensors to float32 when using the MPS device, as MPS does not support float64.
- Added unit tests to verify the automatic conversion of float64 tensors to float32 and ensure compatibility with various tensor types on the MPS device.
---------
Co-authored-by: AdilZouitine <adilzouitinegm@gmail.com>
---------
Signed-off-by: Adil Zouitine <adilzouitinegm@gmail.com>
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com>
Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>
Co-authored-by: Steven Palma <steven.palma@huggingface.co>
Co-authored-by: Pepijn <pepijn@huggingface.co>
2025-09-18 15:25:26 +02:00
"lerobot[phone]" ,
2025-09-22 15:36:20 +02:00
"lerobot[libero]" ,
2025-10-14 17:21:18 +02:00
"lerobot[metaworld]" ,
2025-12-18 12:50:32 +01:00
"lerobot[sarm]"
2025-07-17 18:07:07 +02:00
]
2025-07-25 12:06:46 +02:00
[ project . scripts ]
2025-09-24 14:06:48 +02:00
lerobot-calibrate = "lerobot.scripts.lerobot_calibrate:main"
2025-09-24 11:14:48 +02:00
lerobot-find-cameras = "lerobot.scripts.lerobot_find_cameras:main"
2025-09-24 11:38:04 +02:00
lerobot-find-port = "lerobot.scripts.lerobot_find_port:main"
2025-09-24 13:38:12 +02:00
lerobot-record = "lerobot.scripts.lerobot_record:main"
2025-09-24 14:48:23 +02:00
lerobot-replay = "lerobot.scripts.lerobot_replay:main"
2025-09-24 14:06:58 +02:00
lerobot-setup-motors = "lerobot.scripts.lerobot_setup_motors:main"
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lerobot-teleoperate = "lerobot.scripts.lerobot_teleoperate:main"
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lerobot-eval = "lerobot.scripts.lerobot_eval:main"
lerobot-train = "lerobot.scripts.lerobot_train:main"
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lerobot-dataset-viz = "lerobot.scripts.lerobot_dataset_viz:main"
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lerobot-info = "lerobot.scripts.lerobot_info:main"
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lerobot-find-joint-limits = "lerobot.scripts.lerobot_find_joint_limits:main"
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lerobot-imgtransform-viz = "lerobot.scripts.lerobot_imgtransform_viz:main"
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lerobot-edit-dataset = "lerobot.scripts.lerobot_edit_dataset:main"
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# ---------------- Tool Configurations ----------------
[ tool . setuptools . packages . find ]
where = [ "src" ]
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[ tool . ruff ]
target-version = "py310"
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line-length = 110
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exclude = [ "tests/artifacts/**/*.safetensors" , "*_pb2.py" , "*_pb2_grpc.py" ]
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[ tool . ruff . lint ]
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# E, W: pycodestyle errors and warnings
# F: PyFlakes
# I: isort
# UP: pyupgrade
# B: flake8-bugbear (good practices, potential bugs)
# C4: flake8-comprehensions (more concise comprehensions)
# A: flake8-builtins (shadowing builtins)
# SIM: flake8-simplify
# RUF: Ruff-specific rules
# D: pydocstyle (for docstring style/formatting)
# S: flake8-bandit (some security checks, complements Bandit)
# T20: flake8-print (discourage print statements in production code)
# N: pep8-naming
# TODO: Uncomment rules when ready to use
select = [
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"E" , "W" , "F" , "I" , "B" , "C4" , "T20" , "N" , "UP" , "SIM" #, "A", "S", "D", "RUF"
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]
ignore = [
"E501" , # Line too long
"T201" , # Print statement found
"T203" , # Pprint statement found
"B008" , # Perform function call in argument defaults
]
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[ tool . ruff . lint . per-file-ignores ]
"__init__.py" = [ "F401" , "F403" ]
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"src/lerobot/policies/wall_x/**" = [ "N801" , "N812" , "SIM102" , "SIM108" , "SIM210" , "SIM211" , "B006" , "B007" , "SIM118" ] # Supprese these as they are coming from original Qwen2_5_vl code TODO(pepijn): refactor original
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[ tool . ruff . lint . isort ]
combine-as-imports = true
known-first-party = [ "lerobot" ]
[ tool . ruff . lint . pydocstyle ]
convention = "google"
[ tool . ruff . format ]
quote-style = "double"
indent-style = "space"
skip-magic-trailing-comma = false
line-ending = "auto"
docstring-code-format = true
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[ tool . bandit ]
exclude_dirs = [
"tests" ,
"benchmarks" ,
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"src/lerobot/datasets/push_dataset_to_hub" ,
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]
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skips = [ "B101" , "B311" , "B404" , "B603" , "B615" ]
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[ tool . typos ]
default . extend-ignore-re = [
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"(?Rm)^.*(#|//)\\s*spellchecker:disable-line$" , # spellchecker:disable-line
"(?s)(#|//)\\s*spellchecker:off.*?\\n\\s*(#|//)\\s*spellchecker:on" , # spellchecker:<on|off>
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]
default . extend-ignore-identifiers-re = [
# Add individual words here to ignore them
"2nd" ,
"pn" ,
"ser" ,
"ein" ,
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"thw" ,
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"inpt" ,
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"ROBOTIS" ,
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]
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# TODO: Uncomment when ready to use
# [tool.interrogate]
# ignore-init-module = true
# ignore-init-method = true
# ignore-nested-functions = false
# ignore-magic = false
# ignore-semiprivate = false
# ignore-private = false
# ignore-property-decorators = false
# ignore-module = false
# ignore-setters = false
# fail-under = 80
# output-format = "term-missing"
# color = true
# paths = ["src/lerobot"]
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# TODO: Enable mypy gradually module by module across multiple PRs
# Uncomment [tool.mypy] first, then uncomment individual module overrides as they get proper type annotations
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[ tool . mypy ]
python_version = "3.10"
ignore_missing_imports = true
follow_imports = "skip"
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# warn_return_any = true
# warn_unused_configs = true
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# strict = true
# disallow_untyped_defs = true
# disallow_incomplete_defs = true
# check_untyped_defs = true
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[ [ tool . mypy . overrides ] ]
module = "lerobot.*"
ignore_errors = true
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[ [ tool . mypy . overrides ] ]
module = "lerobot.envs.*"
ignore_errors = false
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# [[tool.mypy.overrides]]
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# module = "lerobot.utils.*"
# ignore_errors = false
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[ [ tool . mypy . overrides ] ]
module = "lerobot.configs.*"
ignore_errors = false
# extra strictness for configs
disallow_untyped_defs = true
disallow_incomplete_defs = true
check_untyped_defs = true
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[ [ tool . mypy . overrides ] ]
module = "lerobot.optim.*"
ignore_errors = false
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[ [ tool . mypy . overrides ] ]
module = "lerobot.model.*"
ignore_errors = false
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# [[tool.mypy.overrides]]
# module = "lerobot.processor.*"
# ignore_errors = false
# [[tool.mypy.overrides]]
# module = "lerobot.datasets.*"
# ignore_errors = false
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[ [ tool . mypy . overrides ] ]
module = "lerobot.cameras.*"
ignore_errors = false
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# [[tool.mypy.overrides]]
# module = "lerobot.motors.*"
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# ignore_errors = false
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# [[tool.mypy.overrides]]
# module = "lerobot.robots.*"
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# ignore_errors = false
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# [[tool.mypy.overrides]]
# module = "lerobot.teleoperators.*"
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# ignore_errors = false
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# [[tool.mypy.overrides]]
# module = "lerobot.policies.*"
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# ignore_errors = false
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# [[tool.mypy.overrides]]
# module = "lerobot.rl.*"
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# ignore_errors = false
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# [[tool.mypy.overrides]]
# module = "lerobot.async_inference.*"
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# ignore_errors = false
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[ [ tool . mypy . overrides ] ]
module = "lerobot.transport.*"
ignore_errors = false
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# [[tool.mypy.overrides]]
# module = "lerobot.scripts.*"
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# ignore_errors = false
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[ tool . uv ]
# wallx requires transformers==4.49.0 which conflicts with other extras that need >=4.53.0
conflicts = [
[
{ extra = "wallx" } ,
{ extra = "transformers-dep" } ,
] ,
[
{ extra = "wallx" } ,
{ extra = "pi" } ,
] ,
[
{ extra = "wallx" } ,
{ extra = "smolvla" } ,
] ,
[
{ extra = "wallx" } ,
{ extra = "groot" } ,
] ,
[
{ extra = "wallx" } ,
{ extra = "xvla" } ,
] ,
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[
{ extra = "wallx" } ,
{ extra = "sarm" } ,
] ,
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[
{ extra = "wallx" } ,
{ extra = "hilserl" } ,
] ,
[
{ extra = "wallx" } ,
{ extra = "libero" } ,
] ,
[
{ extra = "wallx" } ,
{ extra = "all" } ,
] ,
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# pi uses custom branch which conflicts with transformers-dep
[
{ extra = "pi" } ,
{ extra = "transformers-dep" } ,
] ,
[
{ extra = "pi" } ,
{ extra = "smolvla" } ,
] ,
[
{ extra = "pi" } ,
{ extra = "groot" } ,
] ,
[
{ extra = "pi" } ,
{ extra = "xvla" } ,
] ,
[
{ extra = "pi" } ,
{ extra = "sarm" } ,
] ,
[
{ extra = "pi" } ,
{ extra = "hilserl" } ,
] ,
[
{ extra = "pi" } ,
{ extra = "libero" } ,
] ,
[
{ extra = "pi" } ,
{ extra = "all" } ,
] ,
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]