* docs: fix code fences in env processor guide
The "Flexibility and Reusability" section wrapped a duplicated example
in a four-backtick fence and left a following block unclosed, so the
stray closing fence matched a later block. Everything in between
rendered as one code block that swallowed the surrounding prose.
Remove the duplicated block, add the missing closing fence after the
first example, and normalize the four-backtick fences to three so all
fences pair correctly.
* docs(pi0fast): fix typo 40kk -> 40k steps
* docs(integrate-hardware): fix so101 follower source link
* docs(hope_jr): fix dataset example link
The "example" link in the Record section pointed at the dataset's
`/settings` page, which returns HTTP 403 for readers. Drop the
`/settings` suffix so it links to the public dataset page the
sentence describes.
* docs(lekiwi): render emoji shortcodes as unicode
MDX does not expand `🤗` / `🤖` shortcodes, so they showed as
literal text in the rendered install step. Replace them with the 🤗 and
🤖 unicode characters, matching how the other robot pages write emoji.
* docs(smolvla): anchor record link to its section
The "Record a dataset" link dropped readers at the top of the
il_robots page instead of the relevant section. Point it at the
`#record-a-dataset` anchor (the `## Record a dataset` heading in
il_robots.mdx) so the link lands on the step it names.
- Fixed broken API examples in Lerobot Imitation Learning Documentation
- Teleoperation with cameras improved by adding a fixed frequency in the loop (without it the cameras feed gets very slow)
- Wrapped record example script in main() to avoid problems on Mac
- Previously teleoperation example was using SO-ARM and teleoperation with cameras was using Koch. I changed it to use SO-ARM in all of the examples.
- Added section on how to train with HF Jobs - CLI and Python examples
- Replaced lerobot-record with lerobot-rollout in policies examples
* chore(video backend): renaming codec into video_backend in get_safe_default_video_backend()
* feat(pyav utils): adding suport for PyAV encoding parameters validation
* feat(VideoEncoderConfig): creating a VideoEncoderConfig to encapsulate encoding parameters
* feat(VideoEncoderConfig): propagating the VideoEncoderConfig in the codebase
* chore(docs): updating the docs
* feat(metadata): adding encoding parameters in dataset metadata
* fix(concatenation compatibility): adding compatibility check when concatenating video files
* feat(VideoEncoderConfig init): making VideoEncoderConfig more robust and adaptable to multiple backends
* feat(pyav checks): making pyav parameters checks more robust
* chore(duplicate): removing duplicate get_codec_options definition
* test(existing): adapting existing tests
* test(new): adding new tests for encoding related features
* chore(format): fixing formatting issues
* chore(PyAV): cleaning up PyAV utils and encoding parameters checks to stick to the minimun required tooling.
* chore(format): formatting code
* chore(doctrings): updating docstrings
* fix(camera_encoder_config): Removing camera_encoder_config from LeRobotDataset, as it's only required in LeRobotDatasetWriter.
* feat(default values): applying a consistent naming convention for default RGB cameras video encoder parameters
* fix(rollout): propagating VideoEncoderConfig to the latest recording modes
* chore(format): formatting code, fixing error messages and variable names
* fix(arguments order): reverting changes in arguments order in StreamingVideoEncoder
* chore(relative imports): switching to relative local imports within lerobot.datasets
* test(artifacts): cleaning up artifacts for the video encoding tests
* chore(docs): updating docs
* chore(fromat): formatting code
* fix(imports): refactoring the file architecture to avoid circular imports. VideoEncoderConfig is now defined in lerobot.configs and lazily imports av at runtime.
* fix(typos): fixing typos and small mistakes
* test(factories): updating factories
* feat(aggregate): updating dataset aggregation procedure. Encoding tuning paramters (crf, g,...) are ignored for validation and changed to None in the aggregated dataset if incompatible.
* docs(typos): fixing typos
* fix(deletion): reverting unwanted deletion
* fix(typos): fixing multiple typos
* feat(codec options): passing codec options to lerobot_edit_dataset episode deletion tool
* typo(typo): typo
* fix(typos): fixing remaining typos
* chore(rename): renaming camera_encoder_config to camera_encoder
* docs(clean): cleaning and formating docs
* docs(dataset): addind details about datasets
* chore(format): formatting code
* docs(warning): adding warning regarding encoding parameters modification
* fix(re-encoding): removing inconsistent re-encoding option in lerobot_edit_dataset
* typos(typos): typos
* chore(format): resolving prettier issues
* fix(h264_nvenc): fixing crf handling for h264_nvenc
* docs(clean): removing too technical parts of the docs
* fix(imports): fixing imports at the __init__ level
* fix(imports): fixing not very pretty imports in video config file
* initial commit
* change device in test
* do detailed import
* adhere to python 3.11 syntax
* fix autodocstring
* additionally
* do same in other files
* add model. prefix to all keys in state dict
* use dummy stats
* add pi05
* also shorten action_steps
* fix test
* all test pass! and fix tokenizer max length between 05 and 0
* remove test
* fix transformer dependency
* fix test
* split pi0 and pi05 policy in seperate files
* fix test
* fix push to hub test
* add some comments, license and readme
* remove warning in config
* add pi05 to factory
* remove check
* rename action_horizon to chunk_size
* clean up padding of state and action (more in line with lerobot pi0)
* add openpi image transforms for training and add more flexibility to _preprocess_images similar to lerobot pi0
* fix key match from pytorch state dict (similar keys to openpi implementation now)
* also for pi05
* update to python 3.11
* revert to openpi transformer replace python 3.11
* fix(modeling pi0): nit warning message
* use safeauto_docstring
* fix: remove unused param
* fix from pretrained
* add preprocess tests
* also compile forward method
* Do not add model prefix to normalization
* use same name for action and state dim as lerobot pi0 and remove fixed image keys
* load from pretrained_path
* temp: hardcode base model
* fix override self.pretrained_path = None overwrite
* rename to loss
* remove additional image augmentations, lerobot dataset already does this
* Add docs
* put tests in test folder
* Add test to instatiate all base models
* go back to python 3.10
* update docs
* adapt docs pi05
* change docs: finetune base model options
* minor docs fixes and dependencies
* remove todo
* cast float64 to float32 for mps
* skip if no transformers
* fix tests
* add new models to modelcard
* add back init
* fix circular input
* feat: only run pi test on GPU
* remove require_nightly_gpu
* replace decorator test_pi0_openpi
* rename action_dim, state_dim to max_action_dim, max_state_dim
* fix doc and constants
* cleanup tests
* fix from pretrained
* fix tests
* add comment pi0 pi05 tests, add image features to pi0 pi05 hub tests
* fix, state is included in language not in flow head
* Move test to specific folder
* and paligemma task with newline
* remove add_special_tokens, not needed
* feedback pr
* Remove previous pi0 and rename pi0_openpi and pi05_openpi
* Add Quantile stats to LeRobotDataset (#1985)
* - Add RunningQuantileStats class for efficient histogram-based quantile computation
- Integrate quantile parameters (compute_quantiles, quantiles) into LeRobotDataset
- Support quantile computation during episode collection and aggregation
- Add comprehensive function-based test suite (24 tests) for quantile functionality
- Maintain full backward compatibility with existing stats computation
- Enable configurable quantiles (default: [0.01, 0.99]) for robust normalization
* style fixes, make quantiles computation by default to new datasets
* fix tests
* - Added DEFAULT_QUANTILES=[0.01, 0.10, 0.50, 0.90, 0.99] to be computed for each features instead of being chosen by the user
- Fortified tests.
* - add helper functions to reshape stats
- add missing test for quantiles
* - Add QUANTILE normalization mode to normalize the data with the 1st and 99th percentiles.
- Add QUANTILE10 normalization mode to normalize the data with the 10th and 90th percentiles.
* style fixes
* Added missing lisence
* Simplify compute_stats
* - added script `augment_dataset_quantile_stats.py` so that we can add quantile stats to existing v3 datasets that dont have quatniles
- modified quantile computation instead of using the edge for the value, interpolate the values in the bin
* rename pi0/pi05 files
* Remove open pi patch and use custom transformer branch for now
* renaming
* fix
* Revert "fix"
This reverts commit 1ea65730ac.
* fix naming
* feet(pi0/pi0.5): add pipeline (#2009)
* feat(processor): convert openpi model with processor
* TODO: Make test works
* fix(modeling_pi0openpi): update attention mask value and time scaling; improve task handling in tests
- Changed the attention mask value from `self.config.attention_mask_value` to a fixed value of `-2.3819763e38`.
- Updated time scaling in the `sample_noise` method to use a constant factor of `0.999` and an offset of `0.001`.
- Enhanced task handling in tests to ensure proper formatting and batch size consistency.
- Cleaned up commented-out test code for clarity.
* refactor(pi0): rename PI0OpenPIConfig and PI0OpenPIPolicy to PI0Config and PI0Policy
- Updated imports and references throughout the codebase to reflect the new naming convention.
- Introduced a new processor file for PI0 to handle pre-processing and post-processing steps.
- Adjusted tests to utilize the renamed classes, ensuring consistency and functionality.
- Enhanced clarity and maintainability by removing outdated naming conventions.
* refactor(pi05): rename PI0OpenPIPolicy to PI0Policy and update configuration
- Renamed `PI0OpenPIPolicy` to `PI0Policy` for consistency with naming conventions.
- Updated the `PI05OpenPIConfig` to include a new `tokenizer_max_length` attribute and changed the normalization mode for state from `MEAN_STD` to `QUANTILES`.
- Simplified model initialization in `PI05OpenPIPolicy` by removing unused `dataset_stats` parameter.
- Added a new processor class for `Pi05PrepareStateTokenizerProcessorStep` with `@dataclass` for improved readability.
- Introduced a test script to compare the integration of the PI0OpenPI policy with the original implementation, ensuring local testing compatibility.
* feat(processor): convert openpi model with processor
* TODO: Make test works
* fix(modeling_pi0openpi): update attention mask value and time scaling; improve task handling in tests
- Changed the attention mask value from `self.config.attention_mask_value` to a fixed value of `-2.3819763e38`.
- Updated time scaling in the `sample_noise` method to use a constant factor of `0.999` and an offset of `0.001`.
- Enhanced task handling in tests to ensure proper formatting and batch size consistency.
- Cleaned up commented-out test code for clarity.
* refactor(pi0): rename PI0OpenPIConfig and PI0OpenPIPolicy to PI0Config and PI0Policy
- Updated imports and references throughout the codebase to reflect the new naming convention.
- Introduced a new processor file for PI0 to handle pre-processing and post-processing steps.
- Adjusted tests to utilize the renamed classes, ensuring consistency and functionality.
- Enhanced clarity and maintainability by removing outdated naming conventions.
* refactor(pi05): rename PI0OpenPIPolicy to PI0Policy and update configuration
- Renamed `PI0OpenPIPolicy` to `PI0Policy` for consistency with naming conventions.
- Updated the `PI05OpenPIConfig` to include a new `tokenizer_max_length` attribute and changed the normalization mode for state from `MEAN_STD` to `QUANTILES`.
- Simplified model initialization in `PI05OpenPIPolicy` by removing unused `dataset_stats` parameter.
- Added a new processor class for `Pi05PrepareStateTokenizerProcessorStep` with `@dataclass` for improved readability.
- Introduced a test script to compare the integration of the PI0OpenPI policy with the original implementation, ensuring local testing compatibility.
* refactor(pi05): update imports and rename configuration classes
- Changed imports to reflect the new naming convention for PI05 configuration and policy classes.
- Renamed `PI05OpenPIConfig` to `PI05Config` and `PI05OpenPIPolicy` to `PI05Policy` for consistency.
- Introduced a new processor file for PI05, implementing pre-processing and post-processing steps.
- Updated tests to utilize the renamed classes, ensuring functionality and consistency across the codebase.
* update(pi05): increase tokenizer_max_length for improved processing
- Changed the `tokenizer_max_length` from 48 to 200 to enhance the model's capability in handling longer sequences.
- This adjustment aims to improve the overall performance and flexibility of the PI05 configuration.
* add default for state (max_state_dim)
* correct naming
* fix import
* cleanup code
* remove unused test
* us quantiles for action
* move to device
* remove discrete state assert
* fix pi05 test
* move pi05 to device
* use base models in comparison tests
* small renames for tests
* change number of tokens pi05 test
* fix openpi tokenization in test
* fix hub test
* fix test
* assert lerobot vs openpi tests
---------
Co-authored-by: Pepijn <pepijn@huggingface.co>
* add headers
* add back previously removed imports
* update if statement load processor with dataset stats
* remove to avoid circular import
* inject dataset stats for pretrained models
* check normalization before applying
* add link to quantile augument script
* fix(policies): transformers import for ci in PI0 & PI05 (#2039)
* fix(policies): transformers import for ci in PI0
* fix(policies): transformers import for ci in PI05
* test(processor): fix expected raise when normalization types are missing (#2040)
* switch normalization order pipeline for pi05
* Fix/quantiles script (#2064)
* refactor augment stats with quantiles script
add parallelization for faster processing
shift the quantile normalization between -1 1
* fix replay buffer tests
* fix comment
* overwrite the pipeline normalization features with the policy features
* remove double normalization overwrite
* cleanup from pretrained
* remove typo
* also set norm_map
* fix(augment_quantiles) images incorrectly divided by 255
* clamp quantiles
* link to lerobot base models
* rename tests
* encorperate PR feedback
* update docstring for RunningQuantileStats
* update doc links
* Revert "clamp quantiles"
This reverts commit 172207471c.
* fix self.paligemma
* fix tests related to quantiles that were scaled to [0,1], the new range is [-1, 1]
* fix libero doc and use different transformer branch
* use fix branch instead of feat
* update results libero
* add new line
* fix formatting
* precommit
* update results libero
* update libero doc
* update title
* final changes
* add quantiles to test
* run pre commit
---------
Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>
Co-authored-by: Adil Zouitine <adilzouitinegm@gmail.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: Steven Palma <steven.palma@huggingface.co>
HUANG TZU-CHUN