mirror of
https://github.com/huggingface/lerobot.git
synced 2026-06-17 08:17:02 +00:00
Pepijn
4dfa8cea65
Port the LingBot-VA policy (Wan2.2 dual-stream video+action world model) into LeRobot, following the EO-1 / VLA-JEPA conventions. Covers inference, checkpoint conversion, and predicted-video saving (training is deferred to a follow-up PR). - Vendored Wan transformer/attention/flex/VAE/scheduler modules (key names preserved for near-identity conversion); torch SDPA default, flashattn/flex lazy-guarded. - LingBotVAConfig (registered "lingbot_va") + processor with fixed-quantile action unnormalization; full dual-stream sampling loop with CFG, two flow-matching schedulers and KV cache, mapped onto select_action with observed-keyframe feedback. - convert_lingbot_va_checkpoints.py (libero/robotwin variants): bundles the ~5B transformer, lazy-pulls the frozen VAE+UMT5 from the source repo. - Predicted-video plumbing in lerobot_eval (predicted_frames_callback; opt-in via --policy.save_predicted_video) and ConstantWithWarmupSchedulerConfig. - pyproject: widen diffusers-dep to <0.37, add lingbot_va + imageio-dep extras, add lingbot_va and (missing) eo1 to `all`. - Factory + policies/__init__ wiring, docs page + toctree, and tests. Note: the LIBERO success-rate correctness gate must be validated on a CUDA GPU with the converted checkpoint. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
185 lines
4.4 KiB
YAML
185 lines
4.4 KiB
YAML
- sections:
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- local: index
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title: LeRobot
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- local: installation
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title: Installation
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- local: cheat-sheet
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title: Cheat sheet
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title: Get started
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- sections:
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- local: il_robots
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title: Imitation Learning for Robots
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- local: lelab
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title: LeLab - Lerobot GUI
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- local: bring_your_own_policies
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title: Adding a Policy
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- local: integrate_hardware
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title: Bring Your Own Hardware
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- local: hilserl
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title: Train a Robot with RL
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- local: hilserl_sim
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title: Train RL in Simulation
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- local: multi_gpu_training
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title: Multi GPU training
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- local: hil_data_collection
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title: Human In the Loop Data Collection
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- local: peft_training
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title: Training with PEFT (e.g., LoRA)
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- local: rename_map
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title: Using Rename Map and Empty Cameras
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title: "Tutorials"
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- sections:
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- local: hardware_guide
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title: Compute Hardware Guide
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- local: torch_accelerators
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title: PyTorch accelerators
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title: "Compute & Hardware"
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- sections:
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- local: lerobot-dataset-v3
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title: Using LeRobotDataset
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- local: porting_datasets_v3
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title: Porting Large Datasets
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- local: using_dataset_tools
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title: Using the Dataset Tools
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- local: language_and_recipes
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title: Language Columns and Recipes
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- local: tools
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title: Tools
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- local: video_encoding_parameters
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title: Video encoding parameters
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- local: streaming_video_encoding
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title: Streaming Video Encoding
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title: "Datasets"
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- sections:
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- local: act
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title: ACT
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- local: smolvla
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title: SmolVLA
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- local: pi0
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title: π₀ (Pi0)
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- local: pi0fast
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title: π₀-FAST (Pi0Fast)
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- local: pi05
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title: π₀.₅ (Pi05)
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- local: molmoact2
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title: MolmoAct2
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- local: vla_jepa
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title: VLA-JEPA
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- local: eo1
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title: EO-1
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- local: lingbot_va
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title: LingBot-VA
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- local: groot
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title: NVIDIA GR00T N1.5
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- local: xvla
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title: X-VLA
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- local: multi_task_dit
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title: Multitask DiT Policy
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- local: walloss
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title: WALL-OSS
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title: "Policies"
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- sections:
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- local: sarm
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title: SARM
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- local: robometer
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title: ROBOMETER
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- local: topreward
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title: TOPReward
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title: "Reward Models"
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- sections:
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- local: inference
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title: Policy Deployment (lerobot-rollout)
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- local: async
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title: Use Async Inference
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- local: rtc
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title: Real-Time Chunking (RTC)
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title: "Inference"
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- sections:
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- local: envhub
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title: Environments from the Hub
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- local: envhub_leisaac
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title: Control & Train Robots in Sim (LeIsaac)
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title: "Simulation"
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- sections:
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- local: adding_benchmarks
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title: Adding a New Benchmark
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- local: libero
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title: LIBERO
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- local: libero_plus
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title: LIBERO-plus
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- local: metaworld
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title: Meta-World
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- local: robotwin
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title: RoboTwin 2.0
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- local: robocasa
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title: RoboCasa365
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- local: robocerebra
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title: RoboCerebra
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- local: robomme
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title: RoboMME
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- local: envhub_isaaclab_arena
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title: NVIDIA IsaacLab Arena Environments
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- local: vlabench
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title: VLABench
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title: "Benchmarks"
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- sections:
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- local: introduction_processors
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title: Introduction to Robot Processors
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- local: debug_processor_pipeline
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title: Debug your processor pipeline
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- local: implement_your_own_processor
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title: Implement your own processor
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- local: processors_robots_teleop
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title: Processors for Robots and Teleoperators
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- local: env_processor
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title: Environment Processors
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- local: action_representations
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title: Action Representations
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title: "Robot Processors"
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- sections:
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- local: so101
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title: SO-101
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- local: so100
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title: SO-100
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- local: koch
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title: Koch v1.1
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- local: lekiwi
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title: LeKiwi
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- local: hope_jr
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title: Hope Jr
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- local: reachy2
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title: Reachy 2
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- local: unitree_g1
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title: Unitree G1
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- local: earthrover_mini_plus
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title: Earth Rover Mini
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- local: omx
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title: OMX
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- local: openarm
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title: OpenArm
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- local: rebot_b601
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title: reBot B601-DM
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title: "Robots"
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- sections:
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- local: phone_teleop
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title: Phone
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title: "Teleoperators"
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- sections:
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- local: cameras
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title: Cameras
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title: "Sensors"
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- sections:
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- local: notebooks
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title: Notebooks
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- local: feetech
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title: Updating Feetech Firmware
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- local: damiao
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title: Damiao Motors and CAN Bus
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title: "Resources"
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- sections:
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- local: contributing
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title: Contribute to LeRobot
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- local: backwardcomp
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title: Backward compatibility
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title: "About"
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