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Steven Palma 698d2a0e77 feat(policies): add EVO1 policy (#3908) 
* feat(policies): add EVO1 policy

* fix(evo1): infer batch size after normalizing image dims

`_collect_image_batches` read `batch_size = batch[camera_keys[0]].shape[0]`
before normalizing per-camera tensors to `(B, C, H, W)`. For an unbatched
`(C, H, W)` input (which the function tries to support via the `image.dim() == 3`
branch), this picked up the channel count `C` instead of the real batch size,
making the subsequent per-sample loop iterate `C` times and indexing go
out of bounds.

Normalize each camera tensor up-front, then read `batch_size` from the
normalized batch dim. Adds `test_collect_image_batches_handles_unbatched_chw`
covering the regression.

Reported by Copilot review on huggingface/lerobot#3545.

* chore(lock): regenerate uv.lock for evo1 extra

Adds the `evo1` entry to `[package.metadata.requires-dist]` and the
`provides-extras` list so that `uv sync --locked --extra test` (used by
fast_tests.yml) no longer reports the lockfile as stale.

Generated with `uv 0.8.0` (matching `UV_VERSION` in fast_tests.yml).
The non-evo1 marker tweaks are produced by `uv lock` re-resolving the
existing dep graph and are not introduced by this PR.

* chore(evo1): align with policy contribution guide conventions

- Add `src/lerobot/policies/evo1/README.md` symlink into `docs/source/evo1.mdx`
  to match the in-tree README convention (mirroring the EO-1 layout).
- Convert `transformers` import in `internvl3_embedder.py` to the standard
  `TYPE_CHECKING + _transformers_available` two-step gating used by other
  optional-backbone policies (e.g. diffusion). The previous lazy-in-`__init__`
  import was functionally equivalent for runtime gating but didn't expose the
  real symbols to type checkers.
- Add `lerobot[evo1]` to the `all` extra in `pyproject.toml` so
  `pip install 'lerobot[all]'` keeps installing every optional policy.

Per the guidance in https://moon-ci-docs.huggingface.co/docs/lerobot/pr_3534/en/contributing_a_policy.

* fix(evo1): finalize policy guide alignment

* docs(evo1): format results table

* Fix EVO1 LIBERO rollout processors

* Fix EVO1 LIBERO eval action postprocessing

* Fix eval action conversion for bf16 policies

* fix(evo1): move LIBERO padding into policy processors

* refactor(evo1): use native HF InternVL3-1B-hf, drop trust_remote_code

- Switch from OpenGVLab/InternVL3-1B (requires trust_remote_code=True)
  to OpenGVLab/InternVL3-1B-hf (native transformers implementation).
- Replace manual _extract_feature + _prepare_and_fuse_embeddings with
  a single model.forward() call — verified bit-for-bit identical output.
- Remove ~170 lines of manual ViT/pixel-shuffle/projection logic.
- Symlink README.md to docs/source/ following repo convention.

Weights are byte-identical between both model variants; only the module
naming differs. All 12 existing unit tests pass. Local training (10 steps)
on maximellerbach/omx_pickandplace confirmed working.

* refactor(policy): evo1 GPU-batched preprocessing +  vectorized attention masking + remove dead code

* fix(style): pre-commit

oops

* chore(evo1): delete added test + reduce diff

* refactor(policies): use config for evo1 + local imports

* refactor(policies): multiple improvements

* chore: update docs + remove legacy codepaths

* feat(policies): implement RTC to EVO1

---------

Co-authored-by: javadcc_mac <javadcc1@sjtu.edu.cn>
Co-authored-by: Yiming Wang <145452074+JAVAdcc@users.noreply.github.com>
Co-authored-by: Martino Russi <nopyeps@gmail.com>
2026-07-03 22:17:15 +02:00

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- sections:
- local: index
title: LeRobot
- local: installation
title: Installation
- local: cheat-sheet
title: Cheat sheet
title: Get started
- sections:
- local: il_robots
title: Imitation Learning for Robots
- local: lelab
title: LeLab - Lerobot GUI
- local: bring_your_own_policies
title: Adding a Policy
- local: integrate_hardware
title: Bring Your Own Hardware
- local: hilserl
title: Train a Robot with RL
- local: hilserl_sim
title: Train RL in Simulation
- local: multi_gpu_training
title: Multi GPU training
- local: hil_data_collection
title: Human In the Loop Data Collection
- local: peft_training
title: Training with PEFT (e.g., LoRA)
- local: rename_map
title: Using Rename Map and Empty Cameras
title: "Tutorials"
- sections:
- local: hardware_guide
title: Compute Hardware Guide
- local: torch_accelerators
title: PyTorch accelerators
title: "Compute & Hardware"
- sections:
- local: lerobot-dataset-v3
title: Using LeRobotDataset
- local: porting_datasets_v3
title: Porting Large Datasets
- local: using_dataset_tools
title: Using the Dataset Tools
- local: language_and_recipes
title: Language Columns and Recipes
- local: tools
title: Tools
- local: annotation_pipeline
title: Annotation Pipeline
- local: video_encoding_parameters
title: Video encoding parameters
- local: streaming_video_encoding
title: Streaming Video Encoding
title: "Datasets"
- sections:
- local: act
title: ACT
- local: smolvla
title: SmolVLA
- local: pi0
title: π₀ (Pi0)
- local: pi0fast
title: π₀-FAST (Pi0Fast)
- local: pi05
title: π₀.₅ (Pi05)
- local: molmoact2
title: MolmoAct2
- local: vla_jepa
title: VLA-JEPA
- local: eo1
title: EO-1
- local: lingbot_va
title: LingBot-VA
- local: fastwam
title: FastWAM
- local: evo1
title: EVO1
- local: groot
title: NVIDIA GR00T
- local: xvla
title: X-VLA
- local: multi_task_dit
title: Multitask DiT Policy
- local: walloss
title: WALL-OSS
title: "Policies"
- sections:
- local: sarm
title: SARM
- local: robometer
title: ROBOMETER
- local: topreward
title: TOPReward
title: "Reward Models"
- sections:
- local: inference
title: Policy Deployment (lerobot-rollout)
- local: async
title: Use Async Inference
- local: rtc
title: Real-Time Chunking (RTC)
title: "Inference"
- sections:
- local: envhub
title: Environments from the Hub
- local: envhub_leisaac
title: Control & Train Robots in Sim (LeIsaac)
title: "Simulation"
- sections:
- local: adding_benchmarks
title: Adding a New Benchmark
- local: libero
title: LIBERO
- local: libero_plus
title: LIBERO-plus
- local: metaworld
title: Meta-World
- local: robotwin
title: RoboTwin 2.0
- local: robocasa
title: RoboCasa365
- local: robocerebra
title: RoboCerebra
- local: robomme
title: RoboMME
- local: envhub_isaaclab_arena
title: NVIDIA IsaacLab Arena Environments
- local: vlabench
title: VLABench
title: "Benchmarks"
- sections:
- local: introduction_processors
title: Introduction to Robot Processors
- local: debug_processor_pipeline
title: Debug your processor pipeline
- local: implement_your_own_processor
title: Implement your own processor
- local: processors_robots_teleop
title: Processors for Robots and Teleoperators
- local: env_processor
title: Environment Processors
- local: action_representations
title: Action Representations
title: "Robot Processors"
- sections:
- local: so101
title: SO-101
- local: so100
title: SO-100
- local: koch
title: Koch v1.1
- local: lekiwi
title: LeKiwi
- local: hope_jr
title: Hope Jr
- local: reachy2
title: Reachy 2
- local: unitree_g1
title: Unitree G1
- local: earthrover_mini_plus
title: Earth Rover Mini
- local: omx
title: OMX
- local: openarm
title: OpenArm
- local: rebot_b601
title: reBot B601-DM
title: "Robots"
- sections:
- local: phone_teleop
title: Phone
title: "Teleoperators"
- sections:
- local: cameras
title: Cameras
title: "Sensors"
- sections:
- local: notebooks
title: Notebooks
- local: feetech
title: Updating Feetech Firmware
- local: damiao
title: Damiao Motors and CAN Bus
title: "Resources"
- sections:
- local: contributing
title: Contribute to LeRobot
- local: backwardcomp
title: Backward compatibility
title: "About"
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