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Steven Palma 708fa1d189 feat(policies): add Gr00t N1.7 policy (#3922) 
* Add GR00T N1.7 support

Add GR00T N1.7 policy configuration, checkpoint compatibility, processor parity, LIBERO documentation, and focused tests.

Co-authored-by: Ryan Halabi <ryhalabi@nvidia.com>

* Move Groot processor compatibility into Groot loader

* Restore GR00T Flash Attention install guidance

* Allow Groot fake RTC chunk prefetch

* Fix GR00T N1.7 RTC action decoding

* Trim GR00T N1.7 RTC chunks to valid horizon

* Ignore padded GR00T N1.7 RTC prefix rows

* removed n1.5 dependency

* removed remaining N1.5 traces

* groot: auto-enable LIBERO gripper action transform for libero_sim

GR00T N1.7 emits gripper in [0,1] but LIBERO expects [-1,1]. The decode
transform existed but was never auto-enabled for embodiment_tag=libero_sim,
so the policy scored 0% on LIBERO eval. Auto-set it in __post_init__ (still
overridable). LIBERO Spatial eval: 0% -> 98%.

* Reconnect GR00T relative action processors

* groot: remove dead N1.5 code (eagle2_hg_model, flow_matching_action_head, action_encoder)

N1.7 backbone is nvidia/Cosmos-Reason2-2B via Qwen3VLForConditionalGeneration,
not Eagle2 — eagle2_hg_model/ had zero refs outside its own dir.

GR00TN17ActionHead (groot_n1_7.py) re-implements MultiEmbodimentActionEncoder +
CategorySpecificLinear + swish + SinusoidalPositionalEncoding locally, so
flow_matching_action_head.py (N1.5 FlowmatchingActionHead) and its sole
dependency action_encoder.py are dead. Verified: no src/ or tests/ reference.

Removed (~2037 LOC):
- eagle2_hg_model/ (4 files, ~1575 LOC)
- action_head/flow_matching_action_head.py (408 LOC)
- action_head/action_encoder.py (54 LOC)

cross_attention_dit.py KEPT (DiT/AlternateVLDiT/SelfAttentionTransformer live in N1.7).

* groot: reuse lerobot get_device_from_parameters instead of inline lookup

modeling_groot.py duplicated next(self.parameters()).device twice. LeRobot
ships get_device_from_parameters in policies/utils.py (used by diffusion,
vqbet, tdmpc, gaussian_actor). Reuse it for consistency with the framework.

* groot: fix stale Eagle VLM docstring in processor (N1.7 uses Qwen3-VL backbone)

Addresses checker nit: processor_groot.py docstring still described the N1.5
Eagle VLM path with eagle_content/eagle_* keys that no longer exist in the code.

* test(groot): add N1.7 original-vs-LeRobot output parity test

Verifies the LeRobot GR00T N1.7 integration produces equivalent raw
action_pred to NVIDIA Isaac-GR00T for the same checkpoint, inputs, seed,
precision (fp32) and attention kernel (SDPA): max|diff|=8.9e-7 on the
libero_sim embodiment (GR00T-N1.7-LIBERO/libero_10).

The two impls pin incompatible transformers majors (orig 4.57.3 vs
LeRobot 5.x) and cannot share a process, so the original outputs + exact
collated inputs are produced out-of-process and loaded from an .npz. The
test skips on CI / when the checkpoint or artifact are absent.

* test(groot): parametrize N1.7 parity across all checkpoint embodiments

Generalize the original-vs-LeRobot N1.7 output-parity test from a single
libero_sim case to every embodiment tag in the checkpoint (libero_sim, oxe_droid,
real_g1, the real_r1_pro_sharpa family, and the xdof family). Inputs are built
generically from checkpoint metadata; the test discovers per-tag .npz artifacts
and runs one parametrized case each, loading the LeRobot model once via a fixture.

All 9 embodiments match the original to fp32 epsilon (max|diff| < 3e-6), confirming
the integration is correct across the model's full embodiment space and not overfit
to libero_sim.

* test(groot): self-contained parity test + in-repo producer + docs

- Rename test_groot_n1_7_vs_original.py -> test_groot_vs_original.py
- Make the test self-contained: producer script (dump_original_n1_7.py) now lives
  next to the test; default artifact dir is repo-relative
  (tests/policies/groot/artifacts/), overridable via GROOT_N1_7_PARITY_DIR. The
  test only reads artifacts and skips if absent -- it never creates external dirs.
- Heavy .npz artifacts (~6-9MB each) are gitignored and regenerated by the producer;
  never committed.
- Drop the verbose 'MULTIPLE EMBODIMENTS' docstring block (kept a one-line note).
- Document the parity procedure in the groot policy README (docs/source/policy_groot_README.md).
- Rename test fn test_groot_n1_7_get_action_parity -> test_groot_get_action_parity.

9/9 embodiments still pass (max|diff| < 3e-6, fp32 eps).

* docs(groot): drop WHY TWO ENVIRONMENTS block from parity test docstring

* test(groot): move parity producer into utils/ package

Mirror the tests/policies/pi0_pi05/utils convention: move dump_original_n1_7.py into
a tests/policies/groot/utils/ package (with __init__.py) and update all path
references in the test docstring/skip-message and the policy README.

* test(groot): adopt test_groot_lerobot for GR00T N1.7, drop N1.5

The test loaded MODEL_PATH='aractingi/bimanual-handover-groot-10k', an N1.5
checkpoint (config base_model_path=nvidia/GR00T-N1.5-3B, no model_version). On
load, model_version defaults to n1.7 while the base path infers n1.5, so the
version-consistency guard in GrootConfig.__post_init__ raised ValueError and both
test_lerobot_groot_inference and test_lerobot_groot_forward_pass failed. N1.5 is no
longer a supported model_version.

Adopt the test for N1.7:
- MODEL_PATH -> nvidia/GR00T-N1.7-3B (root-level sharded safetensors; loads via
  GrootPolicy.from_pretrained as a base N1.7 model).
- Embodiment tag 'gr1' (N1.5) -> 'gr1_unified' (valid N1.7 tag from the checkpoint
  embodiment_id.json), via a single EMBODIMENT_TAG constant.
- DUMMY_ACTION_HORIZON 16 -> 40 to match N1.7's native action-chunk size.
- Docstrings/labels updated to 'GR00T N1.7'.

Both tests run and pass on CUDA; full tests/policies/groot/ suite is
73 passed / 0 failed / 0 skipped.

* docs(groot): document the N1.5 removal and the N1.7 parity test

- groot.mdx: breaking-change warning and migration path (pin lerobot==0.5.1 to
  keep N1.5, or move to N1.7); the dead `huggingface-cli download` is replaced
  with `hf download`.
- policy_groot_README.md: N1.5 removal note, updated paper / model-card links,
  and the two-comparison (model parity + preprocessor parity) description of
  the original-vs-LeRobot test, including the raw-observation artifacts and
  recorded seed.

* fix(groot): N1.7 backbone loading and DiT parameter-count logging

- select_layer default tracks the N1.7-3B checkpoint value (16); real
  checkpoint loads still override it from config.json.
- get_backbone_cls recognizes Cosmos-Reason2 / Qwen3-VL backbones by name and
  warns (instead of silently assuming) when an unrecognized backbone is loaded
  only on the strength of backbone_model_type='qwen'.
- 'revision' pins the GR00T checkpoint repo only and is no longer forwarded
  into the unrelated backbone repo load; pin the backbone via
  transformers_loading_kwargs instead.
- DiT / SelfAttentionTransformer parameter counts go through logging.debug
  instead of print().

* fix(groot): N1.7 config defaults, N1.5 rejection, and processor/model runtime fixes

Covers the GR00T N1.7 source trio (configuration, processor, model wrapper).

Config:
- GrootConfig defaults are the N1.7 values; explicitly passed legacy N1.5-era
  values (chunk_size=50, max_state_dim=64, ...) are remapped with a warning
  instead of silently.
- action_decode_transform gains an 'auto' sentinel so an explicit 'none'
  opt-out wins over the libero_sim default and survives save/load round-trips.
- action_delta_indices is cached on the inputs that determine it.
- Legacy N1.5 checkpoints/configs (tokenizer_assets_repo, model_type/
  architectures/eagle backbone markers) are rejected with a single clear
  error pointing to lerobot==0.5.1.

Processor:
- GrootN17ActionDecodeStep handles the 2-D (B, D) actions delivered by sync
  select_action (relative eef/non-eef decode in eval/record flows).
- Postprocessor falls back to dataset stats when a raw checkpoint lacks the
  configured embodiment tag; raw-state cache is per-instance, not
  process-global; caller overrides (device, rename_map) are honored on the
  raw-checkpoint branch.
- Camera/modality-key mismatches warn (including the zero-match fallback);
  deprecated Qwen2VLImageProcessorFast replaced with Qwen2VLImageProcessor;
  removed N1.5 processor steps are stubbed to raise the removal guidance and
  the action-unpack step is re-registered as _v2.

Model:
- Flash-attention probe is diagnostic-only; forward raises on a missing loss;
  print() replaced with logging; N1.5 base-path mismatch includes the
  removal guidance.

* fix(groot): skip normalization overrides for training

* fix(groot): GPU/tensor N1.7 image preprocessing + resize to trained resolution

GR00T training was dataloader-bound (0->100->0 GPU-utilization sawtooth).
GrootN17VLMEncodeStep ran the Qwen3-VL image processor per frame on PIL images
on the single CPU main-loop thread, and that cost is timed inside dataloading_s
(preprocessor(batch) runs in the main process, not the dataloader workers), so
adding workers cannot hide it.

- Feed the torchvision-backed Qwen3-VL processor (C,H,W) uint8 tensors instead
  of a per-frame Image.fromarray PIL roundtrip, and run resize/normalize/patchify
  on config.device (GPU) when available. Bit-identical on CPU when no resize is
  configured; with a resize only the PIL->torchvision bicubic backend differs
  (<2/255 per pixel). The use_albumentations path stays PIL/cv2; reload on a box
  without the saved device falls back to CPU.

- Default image_target_size/crop to the N1.7 backbone's training geometry
  (256x256 / 230x230) when a checkpoint ships no image sizing (checkpoint_assets
  is None, e.g. finetuning nvidia/GR00T-N1.7-3B via repo-id with a new
  embodiment). Previously image_target_size=None disabled the resize, so
  full-resolution frames were patchified into ~4.7x more vision tokens than the
  model was trained on -- inflating dataloading_s (patchify) and update_s (VLM
  sequence) and skewing the input distribution. Checkpoints that pin their own
  sizing are honored; the default constants are shared with GR00T_N1_7_DEFAULTS.

Net: preprocessing leaves the CPU critical path and the VLM sees the resolution
it was trained on -- faster training/inference and a correct train/serve
distribution. Affects inference too (shared preprocessor); existing checkpoints
still load (backward compatible) but must be retrained to gain the benefits.

* refactor(groot): N1.7 style cleanup (utils, imports, flash-attn, config)

Mechanical refactor of the GR00T N1.7 policy to match the repo's architecture and
style standards. No change to policy algorithm/numerics; only UX/CLI and packaging
changes. Tests are intentionally left untouched (out of scope) and need updating
for the removed `model_version` field.

Cleanup & consolidation:
- Add `groot/utils.py` holding the pure, side-effect-free helpers (JSON I/O, value
  coercion, stat flattening, rot6d/SE3 math, language/batch prep) shared by the
  config and processor layers.
- Remove dead code: the unused `resolve_groot_n1_7_backbone_model` cache-resolver
  cluster, `GR00TN17Config.to_filtered_dict/json`, and the `_copy_default` wrapper.

Imports & execution guards:
- Hoist nested imports to module top; relative imports within the package, absolute
  for external modules. The version-gated Qwen3-VL classes import under the single
  `_transformers_available` guard (transformers is pinned >=5.4, which ships them).
- No import-time side effects: `_register_with_transformers()` now runs in
  `GR00TN17.__init__` (idempotent via `register(exist_ok=True)`), and the N1.5 step
  stubs register lazily before pipeline deserialization (idempotent via the
  registry, no run-once globals).
- Gate optional deps at the point of use with `require_package(..., extra="groot")`.

Dependencies & docs:
- Drop `flash-attn` (and its build-only dep `ninja`) from the `groot` extra; default
  to SDPA (numerically equivalent) with opt-in via `--policy.use_flash_attention`.
  Un-comment `lerobot[groot]` in the `all` extra and regenerate `uv.lock`.
- Rewrite the `groot.mdx` install section: flash-attn is a purely optional,
  user-managed optimization that LeRobot neither installs nor requires.

Config & CLI:
- Surface previously-frozen knobs on `GrootConfig` (plumbed into `GR00TN17Config`;
  no-ops at their defaults): inference — `num_inference_timesteps`, `rtc_ramp_rate`,
  `use_flash_attention`; fine-tuning — `tune_top_llm_layers` (partial-LLM tuning)
  and `tune_vlln` (previously hardwired to True).
- Convert the single-valued `model_version` and `n1_7_backbone_model` fields to
  internal constants.
- Keep `base_model_path`: it is NOT equivalent to `pretrained_path` (raw NVIDIA
  checkpoints have no LeRobot `type` field and load only via `base_model_path`) and
  is genuinely user-tunable.
- Keep the deprecated Isaac-GR00T/N1.5 fields (and the dead LoRA fields) as a
  back-compat block so a v0.5.1 N1.5 `config.json` still parses under draccus and is
  rejected with the friendly N1.5 removal message instead of an opaque decode error.

* Optimize GR00T N1.7 image preprocessing

* Remove PIL fallback from GR00T preprocessing

* Fix GROOT relative action training stats

* Address GROOT relative action review feedback

* Fix GROOT N1.7 relative action stats

* Fix GROOT relative action training stats

* Fix GROOT relative action padding and RTC leftovers

* Reset rollout state after robot episode end

* Revert "Reset rollout state after robot episode end"

This reverts commit 1322f45aec.

* Move GROOT relative stats out of train script

* Guard GR00T relative action stepwise decode

* Match GR00T N1.7 OSS preprocessing and relative actions

* Apply LIBERO action decode override after loading

* Format GR00T OSS parity changes

* chore(policies): add guards, warnings and comments + recover tests n1.5 check

* fix(style): pre-commit

* fix(ci): guard dependecy checks

* chore(groot): move cv2 to the top as its in the default install tag

* chore(policies): add explicit dataset dependecy to gr00t implementation

* fix(test): add guard

* fix(groot): make N1.7 letterbox opt-in

* feat(groot): activate checkpoint-configured N1.7 raw-state dropout during training

Isaac-GR00T applies dual state regularization during fine-tuning: raw-state
zeroing driven by the processor sidecar's state_dropout_prob (0.2 for the
inspected N1.7 checkpoint) plus encoded-feature dropout. Baseline LeRobot kept
the processor in deterministic mode, so the raw-state dropout never activated
(RCA Tier-2 contributor to the LeRobot-trained SO-101 failures).

- GrootN17PackInputsStep: runtime-only 'training' flag + state_dropout_prob;
  whole-sample state zeroing gated on torch.is_grad_enabled() so eval and
  no_grad validation paths are unaffected
- sidecar loader reads state_dropout_prob from processor_config.json
- state_dropout_prob serializes with the step; the training flag intentionally
  does not (reloaded pipelines default to eval, re-enabled only when processors
  are rebuilt with dataset_meta)
- _set_groot_preprocessor_training toggles any dataclass step exposing a
  'training' field on serialized-pipeline reloads

Verification: tests/policies/groot/test_groot_state_dropout.py (4 passed) on
RTX PRO 6000 / CUDA 13.3.

* fix(groot): align N1.7 fine-tuning optimizer/scheduler/precision with Isaac-GR00T

Evidence from the LeRobot-vs-OSS checkpoint comparison: the LeRobot/HF 8k
checkpoint's DiT moved only ~19% as far from base as the OSS-trained one
(0.0547 vs 0.285 relative L2) - undertrained because the scheduler decayed over
a hardcoded 10k steps regardless of --steps, on top of beta1/clip mismatches.

- AdamW betas (0.95, 0.999) -> (0.9, 0.999) and grad_clip_norm 10.0 -> 1.0
  (Isaac defaults)
- scheduler: hardcoded CosineDecayWithWarmup(10k decay, floor 10% peak) ->
  DiffuserSchedulerConfig HF cosine with ceil(max_steps * warmup_ratio) warmup,
  deriving num_training_steps from the outer --steps at runtime
- model_params_fp32 (default true): keep master weights in FP32 and compute
  under BF16 autocast like the native N1.7 recipe (fixes optimizer-update
  numerics vs pure-BF16 params)
- weight-decay grouping via transformers get_parameter_names: biases and norm
  parameters excluded from decay
- restore the TF4 lm_head/embedding weight tie so the unused Qwen LM head stays
  frozen and deduplicated in checkpoints
- action_mask kept in native dtype for the masked flow-matching loss
- drop_n_last_frames: exclude episode tails that cannot supply a complete
  action chunk (Isaac sampler behavior)

Verification: tests/policies/groot/test_groot_training_optim_contract.py
(7 passed) + remaining groot suite 11 passed/5 skipped on RTX PRO 6000 /
CUDA 13.3. Note: tests/policies/groot/test_groot_n1_7.py does not collect on
the base branch (pre-existing ImportError, fixed in PR #37).

* feat(groot): train-time random crop for N1.7 (eval keeps center crop)

Isaac-GR00T crops a random crop_fraction window during training and the
deterministic center window at eval, replaying the sampled window across all
camera views of a sample. This contract is unchanged since the N1.5 release
(gr00t/data/transform/video.py: "If mode is 'train', return a random crop
transform. If mode is 'eval', return a center crop transform.") and mirrors
LeRobot's own Diffusion/VQBeT crop_is_random pattern. The LeRobot N1.7 port
used the eval center crop for training too, so the fine-tuned projector/DiT
never sees frame borders and trains on a single fixed appearance point.

Scope: crop geometry ONLY - no color jitter, no new dependencies. The random
window is plain numpy slicing inside the existing cv2 eval transform:

- _transform_n1_7_image_for_vlm_albumentations gains crop_position=(y, x)
  fractions; None keeps the center crop byte-identical to before (verified
  by test)
- GrootN17VLMEncodeStep gains a runtime-only 'training' flag (never
  serialized; reloaded pipelines default to eval); training samples ONE
  window per sample and reuses it across (timestep, view) frames - Isaac's
  cross-view consistency
- gated on torch.is_grad_enabled() so no_grad validation and frozen-eval
  paths are unaffected
- wired via dataset_meta is not None in make_groot_pre_post_processors and
  the existing _set_groot_preprocessor_training on serialized reloads

Verification: tests/policies/groot/test_groot_train_random_crop.py (8 passed:
center-crop bit-exactness with crop_position=None, corner/center windows,
cross-view replay, train!=eval, no_grad gating, seed reproducibility,
serialization contract) + groot suite 23 passed / 5 skipped on RTX PRO 6000 /
CUDA 13.3.

* docs(groot): update Training & hardware Evaluation commands

Replace the multi-GPU accelerate-launch Training snippet with the current
single-command 'uv run lerobot-train' N1.7 recipe (relative actions excluding
gripper, bf16, flash attention, chunk/n_action_steps=16, bs64/20k steps).

Replace the bimanual 'Evaluate in your hardware setup' rollout example with the
SO-101 follower RTC 'uv run lerobot-rollout' command (strategy.type=base,
inference.type=rtc, wrist+front cameras, place-the-vial task).

Docs-only; no source/test changes.

* docs(groot): parameterize commands with env vars + fill LIBERO results

- Introduce BASE_MODEL / DATASET_ID / REPO_ID / JOB_NAME / OUTPUT_DIR env vars
  in the training command and reuse OUTPUT_DIR + BASE_MODEL in the rollout cmd.
- Fill the LIBERO benchmark table with GR00T-LeRobot success rates
  (Spatial 94%, Object 98%, Goal 93%, LIBERO 10/Long 90%; avg 93.75%),
  drop the OSS column and XX placeholders. LeRobot-focused.

* docs(groot): drop export block, reference env vars directly

Use $DATASET_ID / $BASE_MODEL / $REPO_ID / $OUTPUT_DIR / $JOB_NAME as
bare placeholders in the commands without concrete export assignments.

* docs(groot): keep BASE_MODEL export in training command

* docs(groot): use literal HF repo IDs for dataset/policy repo_id

Public-facing Hub references (--dataset.repo_id, --policy.repo_id) shown as
concrete IDs; local-only values ($OUTPUT_DIR, $JOB_NAME) stay as placeholders.

* docs(groot): add LIBERO training command example

* docs(groot): remove LIBERO checkpoints subdirectory section

* docs(groot): use $BASE_MODEL for base_model_path in LIBERO eval

* docs(groot): drop hf download step from LIBERO eval, fix intro

* docs(groot): restore suite checkpoint download intro sentence

* docs(groot): remove checkpoint download note above LIBERO eval

* docs(groot): update training and rollout commands with new parameters and dependencies

* Add sample so101 training command

* Remove sample so101 training command

* docs(groot): remove optional Flash Attention setup instructions and update base model path for evaluation

* docs(groot): update training command with  image transformation parameters

* docs(groot): add note on inference.queue_threshold value for stable inference

* chore(style): pre-commit gr00t

* docs(groot): update

* chore(policies): minor details

* fix(groot): license headers + test guards

* chore(policies): fix tests

* docs(groot): relative actions param doc

* chore(policy): address some of the AI review items

---------

Co-authored-by: Andrew Wrenn <awrenn@nvidia.com>
Co-authored-by: Ryan Halabi <ryhalabi@nvidia.com>
Co-authored-by: nv-sachdevkartik <ksachdev@nvidia.com>
Co-authored-by: groot-validation <groot-validation@localhost>
Co-authored-by: johnnynunez <johnnynuca14@gmail.com>
Co-authored-by: lbenhorin <lbenhorin@nvidia.com>
2026-07-03 21:15:09 +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: 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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