* fix(record): pass rename_map to make_policy in lerobot-record
Fixes#3181. The rename_map from dataset config was used for preprocessor
construction but not passed to make_policy(), causing feature mismatch
errors when camera key names differ between dataset and model config.
make_policy() already accepts a rename_map parameter and uses it to skip
visual feature consistency validation when remapping is active, but
lerobot_record.py was not passing it through.
* style: fix ruff format for ternary expression
---------
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* feat: HIL data collection, RTC interpolator, and action queue improvements
- Add Human-in-the-Loop (HIL) data collection examples (sync + RTC)
- Add HIL data collection documentation
- Add ActionInterpolator for smoother policy control at higher rates
- Integrate interpolator into lerobot-record and eval_with_real_robot
- Add action queue clear() and get_processed_left_over() methods
- Add rtc/__init__.py for cleaner imports
* docs: expand Related Work section with paper summaries
* fix: only record dataset frames at original fps, not at interpolated rate
The interpolator speeds up robot control (e.g. 2x) but dataset frames
should still be recorded at the original fps. Interpolated-only
iterations now only send actions to the robot without writing to the
dataset.
* refactor: merge HIL sync and RTC scripts into single file with --rtc.enabled toggle
Combines hil_data_collection.py and hil_data_collection_rtc.py into one
script. RTC is toggled via --rtc.enabled=true (defaults to off for sync
inference). Deletes the separate hil_data_collection_rtc.py and updates
docs to reflect the single-script usage.
* test: add ActionInterpolator test suite (29 tests)
Covers constructor validation, passthrough (multiplier=1), 2x and 3x
interpolation with exact value checks, reset/episode boundaries,
control interval calculation, multi-dim actions, and simulated
control loop integration.
* test: add ActionQueue + ActionInterpolator integration tests
Verifies the interpolator doesn't interfere with RTC's leftover chunk
tracking: queue consumption rate matches base fps regardless of
multiplier, get_left_over/get_processed_left_over only change on
queue.get(), merge preserves smooth interpolation across chunks,
and interpolator reset is independent of queue state.
* feat: register SO follower/leader configs in HIL script
Adds SOFollowerRobotConfig and SOLeaderTeleopConfig imports so
SO100/SO101 robots can be used via --robot.type=so_follower
and --teleop.type=so_leader. Updates docs accordingly.
Made-with: Cursor
* docs: remove em dashes from HIL documentation
Made-with: Cursor
* refactor: rename examples/rac to examples/hil
Updates directory name and all references in docs and script docstrings.
Made-with: Cursor
* fix: encorperate pr feedback comments
* refactor(tests): enhance ActionInterpolator test structure and add detailed docstrings
* feedback pr and test fix
* fix(test): pass correct real_delay in interpolator delay test
The test was passing real_delay=0 and relying on _check_delays to
silently override it with the index-based diff. Now passes real_delay=3
to match the 3 actions consumed during the simulated inference period.
* fix pr feedback
* ordering
* update hil script
* fix
* default name
* fix(bi_openarm): use kw_only=True to fix dataclass field ordering
BiOpenArmFollowerConfig overrides `id` with a default, making it
positional in the child — non-default `left_arm_config` then follows a
default field, which Python dataclasses forbid. Adding kw_only=True
(matching the parent RobotConfig) removes positional constraints.
Made-with: Cursor
* style: format long line in hil_data_collection.py
Made-with: Cursor
* pr feedback
---------
Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co>
* refactor(dataset): split reader and writer
* chore(dataset): remove proxys
* refactor(dataset): better reader & writer encapsulation
* refactor(datasets): clean API + reduce leaky implementations
* refactor(dataset): API cleaning for writer, reader and meta
* refactor(dataset): expose writer & reader + other minor improvements
* refactor(dataset): improve teardown routine
* refactor(dataset): add hf_dataset property at the facade level
* chore(dataset): add init for datasset module
* docs(dataset): add docstrings for public API of the dataset classes
* tests(dataset): add tests for new classes
* fix(dataset): remove circular dependecy
* fix(motors): cleanup imports + fix signatures
* feat(motors): add damiao canbus + multiple fixes
* fix(motors): address comments -> last_state + different gains + sleep
* refactor(motors): reduce duplicated code + adressed some comments in the PR
* chore(motors): better timeouts
* tests(motors): damiao test and imports
* chore(deps): fix space
* feat(robot): add openarm leader
Co-authored-by: Pepijn <pepijn@huggingface.co>
* feat(robot): add openarm follower
Co-authored-by: Pepijn <pepijn@huggingface.co>
* refactor(robot): remove mechanical compensations and double arm assumption + rename
* chore(robots): remove left arm references
* refactor(teleop): multiple improvements to leader
* refactor(teleop): multiple improvements to leader
* feat(robots): add open arm to util CLI
* chore(robot): add alias openarm
* Apply suggestions from code review
Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>
Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>
* chore(motors): remove normalization tables damiao
* fix(motors): imports and signatures
* feat(motors): add motor_type_str + recv_id to motor class and _get_motor_recv_id raises if no motor_obj.recv_id
* chore(motors): remove normalize from base motor class and damaio
* tests(motors): remove bad tests (to be replaced)
* chore(motors): updated import check
* fix(robots): open arm mirrored config for joint limits
* chore(motors): update position_kd gain values
* chore(robots): set to 0 if openarm is calibrated at connect time
* chore(robots): remove macos in open arm as can doesn't support it
* chore(robots): update for motor_type_str in Motor class
* chore(robots): no default value for can port in open arms
* feat(robots): add bi manual openarm follower and leader
* use constant for kp and kd range and check responses in mit_control_batch()
* Add docs on setting up canbus and use damiao otor bus, also add lerobot_setup_can.py and log if there is not response from a write command
* precommit format
* supress bandit as these are intentional cli commands
* fix setup-can
* add test
* skip test in ci
* nit precommit
* update doc example
* dont import can for tests
* remove comment
* Add openarms docs
* format
* update purchase link
* can to none if nit availabl;e
* add canfd option in bus
* make handshake logic similar to lerobot-can
* type hint
* type check
* add temp teleop test
* remove script
* mock class
* mock class
* ignore linter
* pre-commit
* Add command for bimanual openarm
* fix import
* fix import leader
* fix import draccus
---------
Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: Pepijn <pepijn@huggingface.co>
Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>
* fix(motors): cleanup imports + fix signatures
* feat(motors): add damiao canbus + multiple fixes
* fix(motors): address comments -> last_state + different gains + sleep
* refactor(motors): reduce duplicated code + adressed some comments in the PR
* chore(motors): better timeouts
* tests(motors): damiao test and imports
* chore(deps): fix space
* feat(robot): add openarm leader
Co-authored-by: Pepijn <pepijn@huggingface.co>
* feat(robot): add openarm follower
Co-authored-by: Pepijn <pepijn@huggingface.co>
* refactor(robot): remove mechanical compensations and double arm assumption + rename
* chore(robots): remove left arm references
* refactor(teleop): multiple improvements to leader
* refactor(teleop): multiple improvements to leader
* feat(robots): add open arm to util CLI
* chore(robot): add alias openarm
* Apply suggestions from code review
Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>
Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>
* chore(motors): remove normalization tables damiao
* fix(motors): imports and signatures
* feat(motors): add motor_type_str + recv_id to motor class and _get_motor_recv_id raises if no motor_obj.recv_id
* chore(motors): remove normalize from base motor class and damaio
* tests(motors): remove bad tests (to be replaced)
* chore(motors): updated import check
* fix(robots): open arm mirrored config for joint limits
* chore(motors): update position_kd gain values
* chore(robots): set to 0 if openarm is calibrated at connect time
* chore(robots): remove macos in open arm as can doesn't support it
* chore(robots): update for motor_type_str in Motor class
* chore(robots): no default value for can port in open arms
* use constant for kp and kd range and check responses in mit_control_batch()
* Add docs on setting up canbus and use damiao otor bus, also add lerobot_setup_can.py and log if there is not response from a write command
* precommit format
* supress bandit as these are intentional cli commands
* fix setup-can
* add test
* skip test in ci
* nit precommit
* update doc example
* dont import can for tests
* remove comment
* Add openarms docs
* format
* update purchase link
* can to none if nit availabl;e
* add canfd option in bus
* make handshake logic similar to lerobot-can
* type hint
* type check
* add temp teleop test
* remove script
* mock class
* ignore linter
---------
Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: Pepijn <pepijn@huggingface.co>
Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>
This PR extends the integration of Unitree g1 with the LeRobot codebase. By converting robot state to a flat dict we can now record and replay episodes (example groot/holosoma scripts need to be adjusted as well). We also improve the simulation integration by calling .step @ _subscribe_motor_state instead of it running in a separate thread. We also add ZMQ camera to lerobot, streaming base64 images over json
* feat(robots): consolidates bi SO setups
* fix(robots): solve circular dependecy
* fix(robots): teleop & record working
* feat(robots): only one SO
* fix(utils): rename bi so
* fix(scripts): bi so import
* fix(rl): remove imports
* 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>
* upload
* feat(omx): simplify motor initialization and remove default calibration files
* feat(omx): read motor positions without normalization for improved accuracy
* update calibration method for return factory value
Signed-off-by: Junha Cha <ckwnsgk1@gachon.ac.kr>
* change the drive mode
* refactor: clean up code by removing unnecessary blank lines in omx_follower and omx_leader modules
* feat(omx): update calibration method to set drive modes for motors
* feat(pyproject): add 'ROBOTIS' to extend-ignore-identifiers-re list
* feat(omx): enhance calibration method to write default drive modes to motors
* Update src/lerobot/robots/omx_follower/__init__.py
Add informations about the robot
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
Signed-off-by: Woojin Wie <dnldnwls1123@gmail.com>
---------
Signed-off-by: Junha Cha <ckwnsgk1@gachon.ac.kr>
Signed-off-by: Woojin Wie <dnldnwls1123@gmail.com>
Co-authored-by: Junha02 <chajunha2023@naver.com>
Co-authored-by: Junha Cha <ckwnsgk1@gachon.ac.kr>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* feat: Add EarthRover Mini Plus robot integration with Frodobots SDK
* refactor: Clean up
* refactor: Remove VirtualCamera implementation for EarthRover Mini Plus integration
* fix: Reduce timeout for camera requests
* fix: Add empty cameras dict for compatibility with recording script
* refactor: Remove record.py script for EarthRover Mini Plus use lerobot_record instead
* refactor: Update documentation for EarthRover Mini Plus integration
* refactor keyboard teleoperation
* refactor: Remove angular velocity
* docs: Add documentation for EarthRover Mini Plus integration
* Add earthrover_mini_plus robot to replay and teleoperate scripts
* refactor: Update stop key from Space to X
* refactor: Implement caching for camera frames and robot telemetry data
* refactor
* refactor: Replace string literals with constants for action and observation keys
* Add Earth Rover Mini to robots section in documentation
Co-authored-by: somthecoder sbaner64@gmail.com
Co-authored-by: randomSmarts Aarshsmittal@gmail.com
Co-authored-by: Hassoonu halsae2@illinois.edu
Co-authored-by: Saketh06 saketh.kantipudi@gmail.com
Co-authored-by: sairajshetye sairajshetye2@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>
* fix: expose a function explicitly building a frame for inference
* fix: first make dataset frame, then make ready for inference
* fix: reducing reliance on lerobot record for policy's ouptuts too
* fix: encapsulating squeezing out + device handling from predict action
* fix: remove duplicated call to build_inference_frame and add a function to only perform data type handling (whole conversion is: keys matching + data type conversion)
* fix(policies): right utils signature + docstrings (#2198)
---------
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* feat(devices): add lazy loading for 3rd party robots cameras and teleoperators
Co-authored-by: Darko Lukić <lukicdarkoo@gmail.com>
* feat(devices): load device class based on assumptions in naming
* docs(devices): instructions for using 3rd party devices
* docs: address review feedback
* chore(docs): add example for 3rd party devices
---------
Co-authored-by: Darko Lukić <lukicdarkoo@gmail.com>
* chore: replace hard-coded 'action' values with constants throughout all the source code
* chore(tests): replace hard-coded action values with constants throughout all the test code
* chore: replace hard-coded OBS values with constants throughout all the source code
* chore(tests): replace hard-coded OBS values with constants throughout all the test code
