* feat(rewards): add RewardModelConfig and PreTrainedRewardModel base classes
* refactor(rewards): migrate Classifier from policies/sac/reward_model/ to rewards/classifier/
* refactor(rewards): migrate SARM from policies/sarm/ to rewards/sarm/
* refactor(rewards): add rewards/factory.py and remove reward model code from policies/factory.py
* refactor(rewards): update imports and delete old reward model locations
* test(rewards): add reward model tests and update existing test imports
* fix(rewards): restore full Classifier and SARM implementations
* test(rewards): restore missing CUDA and mixed precision classifier processor tests
* refactor(lerobot_train.py): remove rabc specific configuration and replace it with a generic samplerweight class in lerobot_train
* refactor(lerobot_train.py): add missing sampling weight script
* linter + missing files
* add testing for sampl weighter
* revert some useless changes, improve typing
* update docs
* add automatic detection of the progress path
* remove type exp
* improve comment
* fix: move rabc.py to rewards/sarm/ and update import paths
* refactor(imports): update reward model imports to new module structure
* refactor(imports): update reward model imports to reflect new module structure
* refactor(imports): conditionally import pandas based on availability
* feat(configs): add reward_model field to TrainPipelineConfig and Hub fields to RewardModelConfig
* refactor(policies): remove reward model branches from policy factory and __init__
* refactor(rewards): expand __init__ facade and fix SARMConfig __post_init__ crash
* feat(train): route reward model training through rewards/factory instead of policies/factory
* refactor(train): streamline reward model training logic
* fix(rewards): ensure FileNotFoundError is raised for missing config_file
* refactor(train): update __get_path_fields__ to include reward_model for config loading
* refactor(classifier): remove redundant input normalization in predict_reward method
* fix(train): raise ValueError for non-trainable reward models in train function
* refactor(pretrained_rm): add model card template
* refactor(tests): reward models
* refactor(sarm): update reset method and remove unused action prediction methods
* refactor(wandb): differentiate tags for reward model and policy training in cfg_to_group function
* fix(train): raise ValueError for PEFT usage in reward model training
* refactor(rewards): enhance RewardModelConfig with device handling and delta indices properties
---------
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>
* Add option for pi family models to train with relative actions (relative to state)
* formatting
* add recomputation of stats and option to compute delta stats
* normalzie after delta conversion
* only recompute state for stats
* calulate chunk based stats
* sample 100k
* load from parquet
* sample 1m
* stats per chunck
* fix
* use quantiles
* stats for entire dataset
* fix
* max 1m frames
* compute before dist
* fix multi gpu processor bug
* Fix RTC with delta actions and OpenArms motor_type wiring
* feat: align pi0_fast delta actions with pi0/pi05 and add RTC integration tests
- Add delta_exclude_joints and action_feature_names to PI0FastConfig
- Move to_absolute_actions from modeling to processor pipeline for pi0_fast
- Add delta action detection and logging to eval_with_real_robot.py
- Add delta actions documentation to pi0 and pi05 READMEs
- Fix ruff lint issues in test_delta_actions.py
- Add test_rtc_delta_actions.py (24 tests) covering:
- ActionQueue with delta vs absolute actions
- RTC denoise step with delta leftovers
- Full pipeline roundtrip (delta → RTC → absolute)
- State rebasing approximation bounds
- Non-delta policy compatibility
- Multi-chunk consistency
* chore: clean up test comments, add OpenPI attribution, remove debug logging
- Replace decorative comment separators in test files with plain section headers
- Add attribution comments for 1e-6 epsilon in normalize_processor.py (from OpenPI)
- Remove debug logging blocks from lerobot_train.py
* refactor: extract compute_delta_action_stats into compute_stats.py
Move the ~70-line inline delta action stats block from lerobot_train.py
into a dedicated function in compute_stats.py, where all other stats
computation already lives. The training script now calls it in 6 lines.
* refactor: remove unused get_processed_left_over from ActionQueue
This method was never called outside of tests. Leftover actions for RTC
guidance are always retrieved via get_left_over() (delta/original space).
* revert: remove logging-only changes from eval_with_real_robot.py
The delta actions detection helper and log message added no functional
value — the script already handles delta policies correctly via the
processor pipeline.
* refactor: use ACTION/OBS_STATE constants instead of hardcoded strings
Replace hardcoded "action" and "observation.state" with ACTION and
OBS_STATE from utils.constants in compute_stats.py, dataset_tools.py,
and lerobot_train.py.
* style: remove stray blank lines in training loop
* refactor: move delta action stats to preprocessing step, remove on-the-fly computation
- Remove on-the-fly compute_delta_action_stats from lerobot_train.py
- Rewrite recompute_stats to delegate action stats to compute_delta_action_stats
(chunk-based sampling matching what the model sees during training)
- Add chunk_size parameter to recompute_stats for delta action computation
- Add delta actions documentation to pi0.mdx and pi05.mdx
* feat: add recompute_stats CLI operation to lerobot-edit-dataset
* fix(tests): relax quantile normalization test tolerance for 1e-6 epsilon
* chore: remove agents_memory/pr_details.md from repo
* refactor: rename delta actions to relative actions throughout
What OpenPI calls "DeltaActions" is actually UMI's "relative trajectory"
representation: each action in the chunk is an offset from the current
state, not from the previous action. This avoids error accumulation.
Renamed across all source, tests, docs, and CLI:
- DeltaActionsProcessorStep → RelativeActionsProcessorStep
- to_delta_actions → to_relative_actions
- use_delta_actions → use_relative_actions
- delta_exclude_joints → relative_exclude_joints
- compute_delta_action_stats → compute_relative_action_stats
- delta_action_processor.py → relative_action_processor.py
- test_delta_actions.py → test_relative_actions.py
Kept as-is: AbsoluteActionsProcessorStep (converts TO absolute),
registry ID "delta_actions_processor" (backward compat), and unrelated
delta references (IK pipeline, Robosuite, RA-BC metrics, gym envs).
* docs: add Action Representations guide
Dedicated page explaining absolute, relative, and delta actions with
numerical examples, joint vs EE space, and how to use kinematics
pipelines and the relative action processor. References UMI paper
(Chi et al., 2024) for the terminology.
* docs: remove redundant OpenPI naming note from action representations
* docs: remove opinionated OpenPI reference from delta actions section
* docs: replace ASCII diagram with UMI paper figure
* docs: remove OpenPI reference from action representations
* docs: use HF-hosted image instead of local asset
* docs: clarify figure attribution
* revert: restore original normalization epsilon behavior
The 1e-6 unconditional epsilon change perturbed all normalized values,
breaking backward compatibility tests. The original approach (1e-8 eps
for MEAN_STD, conditional torch.where for QUANTILES) already handles
division by zero correctly without affecting non-degenerate cases.
* fix: restore delta_action_processor.py used by phone/RL teleop
The rename commit incorrectly deleted delta_action_processor.py and
duplicated its classes into relative_action_processor.py. Restore the
original file and import from it instead.
* fix(processor): address PR #2970 review comments
- Remove shebang from relative_action_processor.py (library module, not script)
- Add device alignment in to_relative_actions/to_absolute_actions so _last_state
on CPU doesn't cause cross-device errors when actions are on CUDA
- Rename delta_step → relative_step in AbsoluteActionsProcessorStep for naming
consistency; update factory.py, all processor files, and tests
- Expand _reconnect_relative_absolute_steps docstring to explain why post-hoc
rewiring is needed after deserialization
- Fix off-by-one in compute_stats.py: sample_upper_bound = total_frames - chunk_size + 1
so last valid start index is included and total_frames == chunk_size is not rejected
- Remove redundant NOTE comment in processor_pi05.py (duplicated two lines below)
- Fix pi0_fast processor ordering: move relative_step before NormalizerProcessorStep
so normalizer sees delta actions (matching pi0/pi05); flip postprocessor to
unnormalize → absolute accordingly. Relative stats are now required for all pi models
- Revert use_relative_joint_actions_aloha → use_delta_joint_actions_aloha in
configuration_smolvla.py (preserve existing public API)
- Update action_representations.mdx: add missing joint to 6-DOF example, fix
'based on a figure', clarify pi family ordering, add RTC compatibility section
* update rtc link
* feat: compute relative action stats over full dataset with optional parallelism
Remove the 100k sample cap from compute_relative_action_stats and process
all valid chunks. Vectorize with numpy (pre-load actions/states, fancy
indexing + broadcasting) for a large speedup over the per-index HF dataset
loop. Add num_workers param for thread-based parallelism (numpy releases
the GIL). Update docs to show --push_to_hub for recompute_stats.
* style: apply ruff formatting to compute_stats.py
* testing on real robot
* style: fix ruff format and remove redundant .keys() calls
Add a `cudnn_deterministic` flag to `TrainPipelineConfig` (default: False)
that sets `torch.backends.cudnn.deterministic = True` and disables benchmark
mode, eliminating CUDA floating-point non-determinism at the cost of ~10-20%
training speed. When False (default) the existing benchmark=True behaviour
is preserved.
* move peft config from `lerobot_train` to policy level
* Update src/lerobot/scripts/lerobot_train.py
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Signed-off-by: Michel Aractingi <michel.aractingi@huggingface.co>
* copilot response
* Change the polciy function to return targets rather than peft config.`_get_default_peft_targets()` override in PI0, PI0.5, SmolVLA
* remove none check when building config dict
---------
Signed-off-by: Michel Aractingi <michel.aractingi@huggingface.co>
* Add basic support for PEFT adapter methods
This changes adds support for training policies with much less parameters
by applying adapter methods such as LoRA on specific parts of the policies
and therefore possibly higher learning rates / batch sizes.
To make this as accessible as possible I thought it useful to provide
defaults for `target_modules` and `modules_to_save`. Currently only SmolVLA
has such defaults but when we agree that this change is useful I will set
out to generate more such defaults. While the user can override these
settings, they are expected to only change the peft_method, rank and init_type
parameters.
* Implement loading of PEFT adapters
Loading a PEFT adapter is currently done by initializing a policy with default config
and then applying the adapter on the resulting model. This has the obvious drawback
that any configurations done during training are not applied in the adapted model.
Currently the `use_peft` attribute of `PreTrainedConfig` is only set during loading
to signal the following code that it has to deal with a PEFT adapter. However
we could imagine a scenario where this is already set at training time and stored
alongside the adapter.
* Store policy config alongside PEFT checkpoint
Before this change the PEFT-wrapped policy did not save the policy's config
alongside the adapter config / weights which prevented us from changing the
policy config. Now the policy config is saved both in full training and PEFT
training.
This change makes loading the PEFT policy adapter much easier as well.
* Add default config for ACT
* Support targets like `all-linear`
* Formatting
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix failing tests
* Remove PEFT compatibility changes in config
We'll wait for the PEFT release that fixes this for good.
* Remove `use_peft` parameter from training script
Instead we make the PEFT config optional which has the same effect.
* Log adapter config to WandB
* Better documentation for CLI arguments
* Don't unload & merge the PEFT model
This can make things hard when using quantized layers (user expects quantized base layers with
unquantized adapters for example, merging defaults to upcast the layers leading to higher
memory).
* Correct way of identifying when to save config
* Add CLI end-to-end tests
Currently there don't seem to be any way to test the CLI commands.
Since this change mostly happens in those I thought it best to add
a way to test these commands end-to-end.
More integrated commands like `lerobot-record` need patching but
standalone commands like training seem to work fine.
* Update default targets
Removed ACT since it doesn't make sense to fine-tune ACT without having it pretrained beforehand.
SmolVLA and Pi0/0.5 are much more senseful targets.
* Clean up loading code
- Centralized instantiation of the PEFT wrapper in `make_policy` for inference
(e.g. in `lerobot-record`)
- Training a PEFT policy also sets `cfg.use_peft` so that all inference code loading
the policy can rely on that attribute to identify if PEFT loading is needed
- Modified RTC example to also include PEFT policies. Mostly because this is an example
I'm currently exploring.
* Make sure push_to_hub works
Since PEFT only wraps `push_to_hub` and not `push_model_to_hub`, the reference
to `self` in `policy.push_model_to_hub` is the unwrapped policy which, of course,
doesn't know anything about PEFT.
To make the upload process aware of PEFT, we pass the unwrapped policy down to
`push_model_to_hub` as a kwarg. This is not ideal but I think it is the best way
for now.
* formatting
* Warn when encountering from-scratch-training
* Revamp pretrained model loading
There were quite a few factors that convinced me that the status quo
is able to load pretrained models from the PEFT adapter config but
in fact that didn't work.
This commit fixes the following things:
- policies wrapped in PEFT will now have a `name_or_path` attribute
containing the name or path of the pretrained model we're fine-tuning
- we further assume that SmolVLA without `pretrained_path` and
`load_vlm_weights==False` must be an user-side error
- we assume that using PEFT on from-scratch-policies must be
an user-side-error
* Make it possible to unset policy features
This is necessary to train pre-trained policies on new datasets so that the
features are inferred from the new dataset and not from the pretrained
policy.
* Use correct loading for PEFT in RTC example
* Make it possible to use PeftModels in eval
* Add test checking that PEFT actually reduces params
* Adapt state/action projections instead of full-finetuning
There doesn't seem to be a benefit to fully fine-tune these layers
over just adapting them, so we do that instead.
* Disallow PEFT training on non-pretrained policies
At first I thought it would make sense to have this feature
in case you want to fine-tune a pre-trained section but in the
end it makes more trouble than it's worth.
It's still possible to allow this in the future when a concrete
need arises.
* Add basic documentation
* Formatting
* Add peft as extra dependency, mark tests
Fast tests currently fail because of the missing dependency.
* Fix pre-commit issues
* Add walx <> peft conflict for uv
* Exclude peft from pi install for now
---------
Co-authored-by: nemo <git@ningu.net>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>
* add initial modeling
* make rewind pretrained policy
* add annotation
* small fix
* add sarm
* subtasks
* fix spawn
* fix rewind discrepancies
* Add script to generate embedding for dataset (#2138)
* Add generate and validate script
* fix precommit
* Improve generate embeddings function by using dataset tools (#2206)
---------
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>
* cleanup
* change order train log
* print batch size
* update sarm processor
* add reward output
* change expected features
* add image validation
* change validation
* get state input from dataset stats
* raise if no state key is found
* pass stats
* cleanup and refactor
* add episode inddex to complementary data
* add subtask init and detection
* revert lerobot_train changes
* pass dataset metadata to policy
* change loadig subtasks
* add small logging
* fix progress conversion and adding initial frame
* use large offset for initial frame (ugly)
* Remove rewind, use clip tokenizer
* add tests, implement formula 1,2 correctly and cleanup
* use task from dataset, cleanup visualizer
* simplify
* simplify and cleanup code and move compute_temporal_proportions to utils
* fix normalization in visualization
* Fix visualization and change prompt
* fix formatting
* add visualize subtask annotations
* use qwen thinking
* try different prompt
* format
* update prompt
* higher temp, long output
* different settings
* use instruct
* show full resp
* split message
* Temp: increase tolerance dataset
* Fix RA-BC (#2572)
* Add next observation loading for RA-BC progress deltas
* Compute weights based on temporal progress deltas instead of static rewards
* Add hard-masking for negative progress deltas in weight computation
* Feat/add dual head (#2582)
* Add dual dense sparse head and annotation
* Add docs
* add dual to procesor
* cleanup
* change sampling in visualize and cleanup
* remove validation
* remove compile
* Feat/test uniform (#2587)
* test uniform
* add different string for misaligned
* Fix rewind and add tests
* uncomment text implementation
* run precommit
* Add head mode for ra-bc
* fix visalization of single task
* add
* return per sample loss
* Fix RA_BC (#2602)
* update rabc implementation
* compute rabc beforehand
* fix import
* add only progress calulation
* use precomputed progress
* multi gpu processing
* import
* fix dataset meta data extraction
* add logging
* logging
* log
* progress per episode
* split differently
* move clip to gpu
* pre decode frames for an episode
* fix cuda initalization
* fix import
* multi processing
* rename
* fix import
* fix
* fix rabc
* use last known progress if oob
* use last known progress if oob
* add misalignment loss with random embeddings
* discard previous changes
* add selection of models to docs for ra_bc
* add transformers dep
* extend tolerance
* initial commit with new codebase
* add tests
* fix
* remove temporal sampler
* drop last frame for sampler
* use original ref
* some fixes
* fix visualization
* remove smoothing and fix order subtasks
* add stride rabc computation
* add push to hub
* add explanation
* add kappa expllaination
* better rabc logging
* feedback pr
* remove dataset tolerance
* revert dataset tool
* revert dataset changes
* add credit
* run precommit
* change path for generate ra_bc
* fix type
* include sarm in all in pyproject
* fix precommit
* lazy import matplotlib
* lazy import qwen
* remove rich console
* skip if transformers is not installed?
* run only when we have faker
* place transformer lazy loading
* Dont test if low transformer version
* fix
* increase transformer
* increase as 4.57.0 is yanked
* remove pi from all
* go back
---------
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>
Co-authored-by: s1lent4gnt <kmeftah.khalil@gmail.com>
* feat: Register external policies
* ruff fix
* move policy util functions to policy factory
* refactor register_third_party_devices -> register_third_party_plugins
* feat: Update docs with bring your own policies
* Improve docs for new policies
* fix: Inconsistent quotation marks
* fix: Remove print statement
* fix: wrong base class name in documentation
* fix: Handle better how the models are parsed
* fix: precommit passing
* Update docs/source/bring_your_own_policies.mdx
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
Signed-off-by: Daniel San José Pro <42489409+danielsanjosepro@users.noreply.github.com>
---------
Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>
Signed-off-by: Daniel San José Pro <42489409+danielsanjosepro@users.noreply.github.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* filter episodes in load_nested_dataset
* nit
* remove test filtering
* move import to module level
* added missing episode indices to the EpisodeAwareSampler in lerobot_train.py;
* Enhance training and logging functionality with accelerator support
- Added support for multi-GPU training by introducing an `accelerator` parameter in training functions.
- Updated `update_policy` to handle gradient updates based on the presence of an accelerator.
- Modified logging to prevent duplicate messages in non-main processes.
- Enhanced `set_seed` and `get_safe_torch_device` functions to accommodate accelerator usage.
- Updated `MetricsTracker` to account for the number of processes when calculating metrics.
- Introduced a new feature in `pyproject.toml` for the `accelerate` library dependency.
* Initialize logging in training script for both main and non-main processes
- Added `init_logging` calls to ensure proper logging setup when using the accelerator and in standard training mode.
- This change enhances the clarity and consistency of logging during training sessions.
* add docs and only push model once
* Place logging under accelerate and update docs
* fix pre commit
* only log in main process
* main logging
* try with local rank
* add tests
* change runner
* fix test
* dont push to hub in multi gpu tests
* pre download dataset in tests
* small fixes
* fix path optimizer state
* update docs, and small improvements in train
* simplify accelerate main process detection
* small improvements in train
* fix OOM bug
* change accelerate detection
* add some debugging
* always use accelerate
* cleanup update method
* cleanup
* fix bug
* scale lr decay if we reduce steps
* cleanup logging
* fix formatting
* encorperate feedback pr
* add min memory to cpu tests
* use accelerate to determin logging
* fix precommit and fix tests
* chore: minor details
---------
Co-authored-by: AdilZouitine <adilzouitinegm@gmail.com>
Co-authored-by: Steven Palma <steven.palma@huggingface.co>
* 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 1ea65730ac.
* 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 172207471c.
* 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>
