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test/rl-processor
149 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 Maxime Ellerbach
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9bc2df80bb |
chore(docs): adding a jupyter notebook that gives you ready-to-paste commands (#3395)
* chore(docs): adding an example quickstart jupyter notebook that gives you ready-to-paste commands * some fixes in the commands * uv lock * Adding notebook to all Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> Signed-off-by: Maxime Ellerbach <maxime@ellerbach.net> * uv lock again --------- Signed-off-by: Maxime Ellerbach <maxime@ellerbach.net> Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> |
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Steven Palma
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df0763a2bc | feat(dependencies): minimal default tag install (#3362) | ||
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Pepijn
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818892a38b |
feat(dagger): Add HIL/Dagger/HG-Dagger/RaC style data collection (#2833)
* 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> |
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Pepijn
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2cf08b7a4b |
Add create reward visualization (#3155)
* Add create reward visualization and multimodal analysis tool * add example for creating progress video for sarm * nit * precommit * refactor: address review comments on create_progress_videos.py - Add shebang and Apache 2.0 license header - Replace hardcoded absolute OUTPUT_DIR with relative default (./progress_videos) - Add argparse CLI (--repo-id, --episode, --camera-key, --output-dir, --gif) - Wrap entrypoint in def main() - Replace all print() with logging - Use logging.error/warning instead of traceback.print_exc - Release VideoCapture via try/finally; consolidate triple-open into single seek - Eliminate intermediate clip file: seek directly via CAP_PROP_POS_MSEC - Make MP4 the default output, GIF opt-in via --gif flag - Add return types to all functions - Add Args/Returns docstrings - Use descriptive variable names throughout Made-with: Cursor * refactor: move create_progress_videos.py to examples/dataset/ for consistency Made-with: Cursor * refactor: address PR review comments on create_progress_videos.py - Replace Unicode ellipsis and multiplication sign with ASCII equivalents - Fix step numbering from 1-5 to 1-4 (only 4 actual steps) - Move frame_width reading into convert_mp4_to_gif - Remove unused text_height variable Made-with: Cursor |
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Pepijn
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15934d8d08 |
feat(policies): add relative action support for pi0, pi0.5, and pi0_fast (#2970)
* 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 |
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Steven Palma
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123495250b |
refactor(dataset): split LeRobotDataset into DatasetReader & DatasetWriter (+ API cleanup) (#3180)
* 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 |
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Steven Palma
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d90e4bcfd3 |
refactor(dataset): modular files (#3171)
* refactor(dataset): modular files * refactor(dataset): update imports across the codebase |
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Steven Palma
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a07b1d76f1 | chore(dependecies): untangle dependecies across internal modules (#3149) | ||
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Martino Russi
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4f2ef024d8 |
feat(robots): Unitree G1 WBC implementation (#2876)
* move locomotion from examples to robot, move controller to teleoperator class * modify teleoperate to send back actions to robot * whole body controller * add holosoma to locomotros * various updates * update joint zeroing etc * ensure safefail with locomotion * add unitree locomotion * launch camera from g1 server * publish at varying framerates * fix async read in camera * attempting to fix camera lag * test camera speedup * training * inference works * remove logging from pi0 * remove logging * push local changes * testing * final changes * revert control_utils * revert utils * revert * revert g1 * revert again: * revert utils * push recents * remove examples * remove junk * remove mjlog * revergt edit_dataset * Update lerobot_edit_dataset.py Signed-off-by: Martino Russi <77496684+nepyope@users.noreply.github.com> * undo teleop changes * revert logging * remove loggings * remove loogs * revert dataset tools * Update dataset_tools.py Signed-off-by: Martino Russi <77496684+nepyope@users.noreply.github.com> * move gravity to utils * revert changes * remove matplotlib viewer (rerun works fine) * factory revert * send policy action directly * recent changes * implement flexible action space * send empty command if arms are missing * rename locomotion to controller * add init * implement feedback * add feedback for teleoperator * fix ruff * fix ruff * use read_latest * fix zmq camera * revert exo_serial * simplify PR * revert exo_changes * revert camera_zmq * Update camera_zmq.py Signed-off-by: Martino Russi <77496684+nepyope@users.noreply.github.com> * remove frame duplication from zmq server * revert channerfactoryinitialize * keep channelfactoryinitialize * remove zeroing out logic * fix typo * refactor teleop class * simplify teleop further * import armindex at the top * fix visualizer again * revert ik helper * push stuff * simplify image_server * update image_server * asd * add threading logic * simplify ik helper stuff * simplify holosoma * fix names * fix docs * revert leg override * clean connect * fix controller * fix ruff * clean teleoperator * set_from_wireless * avoid double initializations * refactor robot class * fix pre-commit * update docs * update docs format * add teleop instructions * unitree_g1 specific exception in record/teleoperate * add thumbnail to docs * add thumbnail to doc * refactor(unitree): multiple improvements (#3103) * refactor(unitree): multiple improvements * test(unitree): added tests + improved installation instructions * refactor(robots): minor changes unitree robot kinematic * chore(robots): rename g1 kinematics file --------- Signed-off-by: Martino Russi <77496684+nepyope@users.noreply.github.com> Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> Co-authored-by: Steven Palma <imstevenpmwork@ieee.org> Co-authored-by: Steven Palma <steven.palma@huggingface.co> |
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Pepijn
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1a24f770d3 |
Feat/slurm compute rabc script (#3041)
* Add SLURM SARM progress annotation script. Provide a standalone two-stage compute/aggregate pipeline for RA-BC progress generation so large datasets can be processed in parallel and optionally uploaded to the Hub. Made-with: Cursor * fix pr comments * remove comments |
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Caroline Pascal
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8fff0fde7c |
chore(docstrings): fixing deprecated root argument description in LeRobotDataset class (#3035)
* chore(docstrings): fixing deprecated `root` argument docstrings in LeRobotDataset class * chore(draccus): updating draccus CLI help * chore(revert): reverting changes in lerobot_dataset_viz.py --------- Co-authored-by: Steven Palma <imstevenpmwork@ieee.org> |
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Steven Palma
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5f15232271 | chore: remove usernames + use entrypoints in docs, comments & sample commands (#2988) | ||
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Steven Palma
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3409ef0dc2 |
refactor(cameras): cameras API extension (#2808)
* feat(cameras): add new read_latest() method * fix(cameras): fix threading bug + clear state * refactor(cameras): multiple improvements * feat(camera): add context manager to camera base class * chore(camera): slight modifications to opencv * test(cameras): update opencv tests according to the changes * refactor(cameras): reflect desing changes to realsense + deal with depth * test(cameras): fix realsense tests accordingly to new changes * refactor(cameras): update reachymini and zmq accordingly * chore: wrap resource sensitive examples into a try/finally * test(cameras): add test for new read_latest * test(cameras): fix problem with image artifact in opencv tests * test(cameras): fix test_read_latest_high_frequency expectations * Apply suggestions from code review 1 Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com> Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> * chore(cameras): address feedback * feat(cameras): add max_age_ms check in read_latest * test(cameras): fix read_latest tests * chore(redundancies): removing redundancies in Reachy 2 camera class * fix(warmup): replacing the arbitrary time.sleep in by an actual warmup in the RealSense camera class * chore(format): formatting latest changes * chore(warning): adding a "to be implemented" warning for read_latest() in Camera base class * chore(warning): making read_latest() warning message shorter and clearer --------- Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com> |
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sato_shinji
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9919b16b36 |
fix: ensure action tensors are moved to client_device in async training (#2792)
* feat(async_inference): server always sends CPU tensors, client handles device conversion * fix:fix the type annotation of RawObservation in src/lerobot/async_inference/helpers.py * update the import of robot_client --------- Co-authored-by: Sato shinji <wwwsatoshinji@gmail.com> Co-authored-by: Steven Palma <imstevenpmwork@ieee.org> Co-authored-by: KB <kevin-brian.n-diaye@epita.fr> |
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Martino Russi
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6b8d4c75a6 |
Feat/g1 improvements record sim (#2765)
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 |
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Steven Palma
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d791a431fe |
feat(robots): consolidates bi SO setups (#2780)
* 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 |
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Steven Palma
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ccfd609ece |
feat(robots): consolidate SO arms implementation (#2763)
* feat(robots): consolidate SO arms implementation * chore(robots): delete unnecessary init modules |
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Martino Russi
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7e9d05a799 |
add holosoma locomotion (#2669)
Add holosoma locomotion from Amazon-FAR Add reset method to unitree_g1 Format actions as dict Update docs |
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Steven Palma
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e2957d7783 | fix: precise_sleep is never called with negative value (#2757) | ||
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githubnemo
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e670ac5daf |
Add basic PEFT support to train script + record module (#1411)
* 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> |
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Martino Russi
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37f43df88a |
Feat/add unitree g1 robot (#2530)
* add unitree_g1_robot_class * finish locomotion loading code * precommit * separate groot locomotion logic * remove leftover locomotion variable, unify kp kd * format config * properly comment config, example locomotion and unitree_g1 class * ready to review * download policy from the hub in `examples/unitree_g1/gr00t_locomotion` * fix linter * make precommit happy, add ignore flags * linter pt3 * linter pt4 * [done] make precommit happy * fix linter 5 * add docs * push utils * feat(robots): add Unitree G1 humanoid support with ZMQ bridge (#2539) * feat(robots): add Unitree G1 humanoid support with ZMQ bridge - Use JSON + base64 serialization for secure communication instead of pickle - Add documentation section - Rename robot_server to run_g1_server - Add dependecies to pyproject.toml * nit in docs * remove globals use * cast robot data to int/float * ensure robot is connected before changing mode * temperature can be list, average in such case --------- Co-authored-by: Martino Russi <nopyeps@gmail.com> * style nit * remove transform_imu_data * remove scipy dependency * modify toml, add external unitree_sdk2py dep * return actions from send_action * cleaning * add instructions for local deployment * Update src/lerobot/robots/unitree_g1/unitree_g1.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Signed-off-by: Martino Russi <77496684+nepyope@users.noreply.github.com> * update config and readme * update docs * update docs * remove torch import * fix docs * remove ip from docs * add licence header --------- Signed-off-by: Martino Russi <77496684+nepyope@users.noreply.github.com> Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> |
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Steven Palma
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b07160eb1b | feat(utils): precise_sleep() less CPU hungry without sacrificing accuracy (#2526) | ||
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Steven Palma
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17581a9449 | fix(examples): wrap all of them into a main function (#2524) | ||
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Eugene Mironov
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8a915c6b6f |
[RTC] Real Time Chunking for Pi0, Smolvla, Pi0.5 (#1698)
* Add Real-Time Chunking (RTC) support for flow matching models Implement Real-Time Chunking (RTC) for action chunking policies using flow matching denoising. RTC enables smooth action transitions between consecutive chunks by using prefix guidance during denoising. Key features: - RTCProcessor class with denoise_step method for RTC guidance - Tracker system for debug tracking using time-based dictionary storage - RTCDebugVisualizer with comprehensive visualization utilities - Integration with SmolVLA policy for flow matching models - Support for multiple prefix attention schedules (ZEROS, ONES, LINEAR, EXP) - Configurable execution horizon and max guidance weight - Example scripts for dataset evaluation and real-time control Technical details: - Uses autograd-based gradient computation for RTC corrections - Time-based tracking eliminates duplicate step issues - Proxy methods in RTCProcessor for cleaner API - Full integration with LeRobot's policy and dataset systems Files added/modified: - src/lerobot/configs/types.py: Add RTCAttentionSchedule enum - src/lerobot/policies/rtc/: Core RTC implementation - configuration_rtc.py: RTC configuration - modeling_rtc.py: RTCProcessor with denoise_step - debug_handler.py: Tracker for debug information - debug_visualizer.py: Visualization utilities - src/lerobot/policies/smolvla/modeling_smolvla.py: RTC integration - examples/rtc/: Example scripts and evaluation tools 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com> Co-Authored-By: Claude <noreply@anthropic.com> * Fix rtc_config attribute access in SmolVLA Use getattr() to safely check for rtc_config attribute existence instead of direct attribute access. This fixes AttributeError when loading policies without rtc_config in their config. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com> Co-Authored-By: Claude <noreply@anthropic.com> * fixup! Fix rtc_config attribute access in SmolVLA * Add RTCConfig field to SmolVLAConfig Add rtc_config as an optional field in SmolVLAConfig to properly support Real-Time Chunking configuration. This replaces the previous getattr() workarounds with direct attribute access, making the code cleaner and more maintainable. Changes: - Import RTCConfig in configuration_smolvla.py - Add rtc_config: RTCConfig | None = None field - Revert getattr() calls to direct attribute access in modeling_smolvla.py 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com> Co-Authored-By: Claude <noreply@anthropic.com> * Refactor RTC enabled checks to use _rtc_enabled helper Add _rtc_enabled() helper method in VLAFlowMatching class to simplify and clean up RTC enabled checks throughout the code. This reduces code duplication and improves readability. Changes: - Add _rtc_enabled() method in VLAFlowMatching - Replace verbose rtc_config checks with _rtc_enabled() calls - Maintain exact same functionality with cleaner code 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com> Co-Authored-By: Claude <noreply@anthropic.com> * Rename track_debug method to track Simplify the method name from track_debug to just track for better readability and consistency. The method already has clear documentation about its debug tracking purpose. Changes: - Rename RTCProcessor.track_debug() to track() - Update all call sites in modeling_smolvla.py and modeling_rtc.py 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com> Co-Authored-By: Claude <noreply@anthropic.com> * Use output_dir for saving all evaluation images Update eval_dataset.py to save all comparison images to the configured output_dir instead of the current directory. This provides better organization and allows users to specify where outputs should be saved. Changes: - Add os import at top level - Create output_dir at start of run_evaluation() - Save all comparison images to output_dir - Remove duplicate os imports - Update init_rtc_processor() docstring to be more concise 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com> Co-Authored-By: Claude <noreply@anthropic.com> * fixup! Use output_dir for saving all evaluation images * Fix logging buffering and enable tracking when RTC config provided - Add force=True to logging.basicConfig to override existing configuration - Enable line buffering for stdout/stderr for real-time log output - Modify init_rtc_processor to create processor when rtc_config exists even if RTC is disabled, allowing tracking of denoising data 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com> * Refactor SmolVLA plotting to use tracker data instead of local variables Remove local tracking variables (correction, x1_t, error) from the denoising loop and instead retrieve plotting data from the RTC tracker after each denoise step. This makes the code cleaner and uses the tracker as the single source of truth for debug/visualization data. Changes: - Remove initialization of correction, x1_t, error before denoising loop - After each Euler step, retrieve most recent debug step from tracker - Extract correction, x1_t, err from debug step for plotting - Update tracking condition to use is_debug_enabled() method 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com> * Move plotting logic from modeling_smolvla to eval_dataset script Refactor to improve separation of concerns: modeling_smolvla.py changes: - Remove all plotting logic from sample_actions method - Remove viz_xt_axs, viz_vt_axs, viz_x1t_axs parameters - Remove matplotlib and RTCDebugVisualizer imports - Remove viz_fig, viz_axs, denoise_step_counter instance variables - Simplify denoising loop to only track data in rtc_processor eval_dataset.py changes: - Add _plot_denoising_steps_from_tracker helper method - Retrieve debug steps from tracker after inference - Plot x_t, v_t, x1_t, correction, and error from tracker data - Enable debug tracking (cfg.rtc.debug = True) for visualization - Remove viz axes parameters from predict_action_chunk calls modeling_rtc.py changes: - Remove v_t from track() call (handled by user change) Benefits: - Cleaner modeling code focused on inference - Evaluation script owns all visualization logic - Better separation of concerns - Tracker is single source of truth for debug data 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com> * Refactor plotting loging * fixup! Refactor plotting loging * Improve visualization: separate correction plot and fix axis scaling Changes: - Create separate figure for correction data instead of overlaying on v_t - Add _rescale_axes helper method to properly scale all axes - Add 10% margin to y-axis for better visualization - Fix v_t chart vertical compression issue Benefits: - Clearer v_t plot without correction overlay - Better axis scaling with proper margins - Separate correction figure for focused analysis - Improved readability of all denoising visualizations Output files: - denoising_xt_comparison.png (x_t trajectories) - denoising_vt_comparison.png (v_t velocity - now cleaner) - denoising_correction_comparison.png (NEW - separate corrections) - denoising_x1t_comparison.png (x1_t state with error) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com> * fixup! Improve visualization: separate correction plot and fix axis scaling * fixup! fixup! Improve visualization: separate correction plot and fix axis scaling * fixup! fixup! fixup! Improve visualization: separate correction plot and fix axis scaling * Fix traacking * Right kwargs for the policy * Add tests for tracker * Fix tests * Drop not required methods * Add torch compilation for eval_dataset * delete policies * Add matplotliv to dev * fixup! Add matplotliv to dev * Experiemnt with late detach * Debug * Fix compilation * Add RTC to PI0 * Pi0 * Pi0 eval dataset * fixup! Pi0 eval dataset * Turn off compilation for pi0/pi05 * fixup! Turn off compilation for pi0/pi05 * fixup! fixup! Turn off compilation for pi0/pi05 * fixup! fixup! fixup! Turn off compilation for pi0/pi05 * fixup! fixup! fixup! fixup! Turn off compilation for pi0/pi05 * fixup! fixup! fixup! fixup! fixup! Turn off compilation for pi0/pi05 * Add workable flow * Small fixes * Add more tests * Add validatio at the end * Update README * Silent validation * Fix tests * Add tests for modeling_rtc * Add tests for flow matching models with RTC * fixup! Add tests for flow matching models with RTC * fixup! fixup! Add tests for flow matching models with RTC * Add one more test * fixup! Add one more test * Fix test to use _rtc_enabled() instead of is_rtc_enabled() 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * fixup! Fix test to use _rtc_enabled() instead of is_rtc_enabled() * fixup! fixup! Fix test to use _rtc_enabled() instead of is_rtc_enabled() * Add RTC initialization tests without config for PI0.5 and SmolVLA Add test_pi05_rtc_initialization_without_rtc_config and test_smolvla_rtc_initialization_without_rtc_config to verify that policies can initialize without RTC config and that _rtc_enabled() returns False in this case. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * Fix PI0.5 init_rtc_processor to use getattr instead of direct model access 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * Fix SmolVLA init_rtc_processor to use getattr instead of direct model access 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * Fix PI0.5 RTC tests to use quantile stats (q01, q99) for normalization 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * fixup! Fix PI0.5 RTC tests to use quantile stats (q01, q99) for normalization * Fixup eval with real robot * fixup! Fixup eval with real robot * fixup! fixup! Fixup eval with real robot * Extract simulator logic from eval_with real robot and add proper headers to files * Update images * Fix tests * fixup! Fix tests * add docs for rtc * enhance doc and add images * Fix instal instructions --------- Co-authored-by: Ben Zhang <benzhangniu@gmail.com> Co-authored-by: Alexander Soare <alexander.soare159@gmail.com> Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> |
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Michel Aractingi
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784cdae55a |
Fixes in port droid scripts (#2455)
* Fixes in port droid scripts * revert default mem-per-cpu * style nit * fix relative imports * style nit |
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Michel Aractingi
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76a425c600 |
Fix: check_cached_episodes doesn't check if the requested episode video were downloaded (#2296)
* In `check_cached_episodes_sufficient` check whether all the requested video files are downloaded * optimize loop over the video paths * revert example num_workers * Apply suggestion from @Copilot Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Signed-off-by: Michel Aractingi <michel.aractingi@huggingface.co> * set num_workers to zero in example * style nit * reintroduce copilot optim --------- Signed-off-by: Michel Aractingi <michel.aractingi@huggingface.co> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> |
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Francesco Capuano
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326aca0a48 |
Add API Examples (#2289)
* (unscrewing things up) (#2288) * 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) * refactor(envs): add custom-observation-size (#2167) * fix: add MockMotorBus to MockRobot * rl: first drafts * add: all components of HIL SERL * fix: actor block works * fix: less friction, less friction * add: hil-serl complete example * fix: dataset names * fix: restructuring example folder * fix: act works but found bug in how ACT works * fix: same path for both pre and postprocessors * fix: paths * add: example usage for act * add: using ACT example * fix: training examples * fix: using examples * fix: camera index * fix: rename workflows into tutorial so that the path of the files is lerobot/examples/tutorial/... * fix: upload everything in one repo * fix: model name * fix: simplify model path * add: VLAs example --------- Signed-off-by: Francesco Capuano <74058581+fracapuano@users.noreply.github.com> * fix: minor fix using named attributes * fix: change model to act * fix: named attributes for inference frame building * fix: minor fixes to smolvla * fix: small changes to pi0 * remove: old file that should have never been committed (ups sorry sorry) --------- Signed-off-by: Francesco Capuano <74058581+fracapuano@users.noreply.github.com> |
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Michel Aractingi
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8e940bf361 |
Feat/expand add features (#2202)
* make add_feature take multiple features at a time and rename to add_features * - New function: modify_features that was a combination of remove features and add features. - This function is important for when we want to add a feature and remove another so we can do it in one time to avoid copying and creating the dataset multiple times |
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Michel Aractingi
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0c79cf8f4e |
Add missing finalize calls in example (#2175)
- add missing calls to dataset.finalize in the example recording scripts - add section in the dataset docs on calling dataset.finalize |
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Michel Aractingi
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b8f7e401d4 |
Dataset tools (#2100)
* feat(dataset-tools): add dataset utilities and example script - Introduced dataset tools for LeRobotDataset, including functions for deleting episodes, splitting datasets, adding/removing features, and merging datasets. - Added an example script demonstrating the usage of these utilities. - Implemented comprehensive tests for all new functionalities to ensure reliability and correctness. * style fixes * move example to dataset dir * missing lisence * fixes mostly path * clean comments * move tests to functions instead of class based * - fix video editting, decode, delete frames and rencode video - copy unchanged video and parquet files to avoid recreating the entire dataset * Fortify tooling tests * Fix type issue resulting from saving numpy arrays with shape 3,1,1 * added lerobot_edit_dataset * - revert changes in examples - remove hardcoded split names * update comment * fix comment add lerobot-edit-dataset shortcut * Apply suggestion from @Copilot Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Signed-off-by: Michel Aractingi <michel.aractingi@huggingface.co> * style nit after copilot review * fix: bug in dataset root when editing the dataset in place (without setting new_repo_id * Fix bug in aggregate.py when accumelating video timestamps; add tests to fortify aggregate videos * Added missing output repo id * migrate delete episode to using pyav instead of decoding, writing frames to disk and encoding again. Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com> * added modified suffix in case repo_id is not set in delete_episode * adding docs for dataset tools * bump av version and add back time_base assignment * linter * modified push_to_hub logic in lerobot_edit_dataset * fix(progress bar): fixing the progress bar issue in dataset tools * chore(concatenate): removing no longer needed concatenate_datasets usage * fix(file sizes forwarding): forwarding files and chunk sizes in metadata info when splitting and aggregating datasets * style fix * refactor(aggregate): Fix video indexing and timestamp bugs in dataset merging There were three critical bugs in aggregate.py that prevented correct dataset merging: 1. Video file indices: Changed from += to = assignment to correctly reference merged video files 2. Video timestamps: Implemented per-source-file offset tracking to maintain continuous timestamps when merging split datasets (was causing non-monotonic timestamp warnings) 3. File rotation offsets: Store timestamp offsets after rotation decision to prevent out-of-bounds frame access (was causing "Invalid frame index" errors with small file size limits) Changes: - Updated update_meta_data() to apply per-source-file timestamp offsets - Updated aggregate_videos() to track offsets correctly during file rotation - Added get_video_duration_in_s import for duration calculation * Improved docs for split dataset and added a check for the possible case that the split size results in zero episodes * chore(docs): update merge documentation details Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> --------- Co-authored-by: CarolinePascal <caroline8.pascal@gmail.com> Co-authored-by: Jack Vial <vialjack@gmail.com> Co-authored-by: Steven Palma <imstevenpmwork@ieee.org> |
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Steven Palma
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f59eb54f5c | chore: remove unused code (#2062) | ||
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Steven Palma
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d2782cf66b |
chore: replace hard-coded action values with constants throughout all the source code (#2055)
* 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 |
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Steven Palma
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43d878a102 |
chore: replace hard-coded obs values with constants throughout all the source code (#2037)
* 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 |
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Steven Palma
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853cc70194 | chore(utils): remove unused utils legacy functions + rename init_rerun (#2031) | ||
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Steven Palma
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2b59850f15 |
chore(scripts): move record to scripts (#2022)
Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> |
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Steven Palma
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7cf04a5ec3 | chore: move constants to utils (#2016) | ||
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Pepijn
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d65668ff3c |
Add docs for LeRobot Image transforms (#1972)
* Remove unused scripts, add docs for image transforms and add example * fix(examples): move train_policy.py under examples, remove outdated readme parts * remove script thats copied to train folder * remove outdated links to examples and example tests |
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Francesco Capuano
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1bc38be719 |
small tiny nit (#1975)
* small tiny nit Signed-off-by: Francesco Capuano <74058581+fracapuano@users.noreply.github.com> Signed-off-by: Francesco Capuano <74058581+fracapuano@users.noreply.github.com> |
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Adil Zouitine
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78b866116f |
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
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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.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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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
for more information, see https://pre-commit.ci
* 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
for more information, see https://pre-commit.ci
* 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
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* [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
for more information, see https://pre-commit.ci
* 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
for more information, see https://pre-commit.ci
* 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.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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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
for more information, see https://pre-commit.ci
* 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
for more information, see https://pre-commit.ci
* 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.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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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.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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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
for more information, see https://pre-commit.ci
* 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
for more information, see https://pre-commit.ci
* 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
for more information, see https://pre-commit.ci
* refactor(pipeline): Introduce ComplementaryDataProcessor for handling complementary data in transitions
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* 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
* [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
for more information, see https://pre-commit.ci
* 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
for more information, see https://pre-commit.ci
* [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
for more information, see https://pre-commit.ci
* 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
for more information, see https://pre-commit.ci
* 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
for more information, see https://pre-commit.ci
* 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
for more information, see https://pre-commit.ci
* 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
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* [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
for more information, see https://pre-commit.ci
* 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
for more information, see https://pre-commit.ci
* 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
for more information, see https://pre-commit.ci
* 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
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* 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
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* 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
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* 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
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* 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
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Francesco Capuano
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33cad37054 |
Add Streaming Dataset (#1613)
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> |
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Michel Aractingi
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f55c6e89f0 |
Dataset v3 (#1412)
Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com> Co-authored-by: Remi Cadene <re.cadene@gmail.com> Co-authored-by: Tavish <tavish9.chen@gmail.com> Co-authored-by: fracapuano <francesco.capuano@huggingface.co> Co-authored-by: CarolinePascal <caroline8.pascal@gmail.com> |
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Steven Palma
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ce3b9f627e |
chore(docs): prioritize use of entry points in docs + fix nightly badge (#1692)
* chore(docs): fix typo in nightly badge * chore(docs): prioritize the use of entrypoints for consistency |
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Steven Palma
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862a4439ea | chore(examples): remove outdated examples (#1526) | ||
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Steven Palma
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378e1f0338 |
Update pre-commit-config.yaml + pyproject.toml + ceil rerun & transformer dependencies version (#1520)
* chore: update .gitignore * chore: update pre-commit * chore(deps): update pyproject * fix(ci): multiple fixes * chore: pre-commit apply * chore: address review comments * Update pyproject.toml Co-authored-by: Ben Zhang <5977478+ben-z@users.noreply.github.com> Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> * chore(deps): add todo --------- Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> Co-authored-by: Ben Zhang <5977478+ben-z@users.noreply.github.com> |
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Michel Aractingi
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63ddfefa08 | Remove references to lerobot.common (#1432) | ||
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Pepijn
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1522e60f83 |
feat: Add fixes and refactor lekiwi example (#1396)
* feat: Add fixes and refactor lekiwi example * fix: replace repo_id with placeholders * feat: use record_loop for lekiwi, use same control strucutre as record.py * feat: make rerun log more general for lekiwi * fix: add comments record_loop and fix params evaluate.py * fix: add events in evaluate.py * fix: add events 2 * change record to display data * Integrate feedback steven * Add docs merging * fix: add lekiwi name check * fix: integrate feedback steven * fix: list for type * fix: check type list * remove second robot connect * fix: added file when merging * fix(record): account for edge cases when teleop is a list --------- Co-authored-by: Steven Palma <steven.palma@huggingface.co> |
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Simon Alibert
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d4ee470b00 |
Package folder structure (#1417)
* Move files * Replace imports & paths * Update relative paths * Update doc symlinks * Update instructions paths * Fix imports * Update grpc files * Update more instructions * Downgrade grpc-tools * Update manifest * Update more paths * Update config paths * Update CI paths * Update bandit exclusions * Remove walkthrough section |
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Pepijn
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10b7b35325 | Match motor names with ids lekiwi (#1261) | ||
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Simon Alibert
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95df341b4f | Fix LeKiwi example (#1217) | ||
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Simon Alibert
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e23b41e79a |
Hardware API redesign (#777)
Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Steven Palma <imstevenpmwork@ieee.org> 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> Co-authored-by: Adil Zouitine <adilzouitinegm@gmail.com> Co-authored-by: Pepijn <pepijn@huggingface.co> |