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58 Commits
| Author | SHA1 | Message | Date | |
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 Pepijn
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1ff10b935c |
Merge branch 'feat/language-annotation-pipeline' into feat/smolvla-on-steerable
Resolves conflicts from 66 commits on the base branch: * pyproject.toml — keep base's transformers>=5.4.0,<5.6.0; add the sentencepiece-dep entry pi052 (FAST action tokenizer) needs. * policies/__init__.py — keep pi052 export; drop the RewardClassifierConfig export that base removed. * policies/factory.py — docstring list resolution (keep pi052; drop reward_classifier, removed by base). * annotations/steerable_pipeline/executor.py — adopt base's renamed _ensure_annotation_metadata_in_info (it already advertises the say tool); drop pi052's older _ensure_tools_in_info call. * configs/train.py — keep pi052's vqa_target_fraction; adopt base's SampleWeightingConfig (legacy RA-BC inline params already covered by the migration shim base added). * scripts/lerobot_train.py — merge pi052's per-policy processor rebuild + dataset_repo_id pass-through with base's active_cfg / is_reward_model_training tightening, and re-route vqa-weighted sampler to active_cfg.drop_n_last_frames. * datasets/language_render.py — adopt base's _select_one + timestamp tolerance (drops pi052's stale _select_latest / per-style sort_key). * tests — adopt base's parametrized per-camera blend + tolerance test; drop pi052 tests that overlap with base's tighter rewrites; keep pi052's flow-only / VQA-blend coverage; add a test_canonical_recipe_loads check on subtask_mem_vqa_speech.yaml. * policies/pi052/processor_pi052.py — import RenderMessagesStep directly from render_messages_processor (base intentionally dropped it from lerobot.processor's re-exports). * uv.lock — regenerated cleanly from base + pi052's pocket-tts / beartype. All 67 touched tests pass (30 pi052 + 37 recipe / language-render / pipeline / render-messages). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Pepijn
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a088c10c80 |
examples(port_datasets): SLURM+datatrove RoboCasa composite_seen build
Parallel variant of build_robocasa_composite_seen.py modeled after the
existing slurm_port_shards.py / slurm_aggregate_shards.py pattern.
Two-phase datatrove pipeline:
* Phase 1 DOWNLOAD: tasks=16 (one per RoboCasa composite_seen task),
each worker downloads its assigned tar via RoboCasa's own
download_datasets helper. Network-bound, idempotent.
* Phase 2 AGGREGATE: tasks=1, single worker calls aggregate_datasets
over the 16 extracted directories. Submitted with depends=phase1 so
SLURM only releases it once all 16 downloads succeed.
Reuses the COMPOSITE_SEEN_TASKS list and per-task download/resolve
helpers from the single-machine script via aliased imports — single
source of truth for 'what does it mean to download a composite_seen
task'.
Local (--slurm 0) mode runs the two phases sequentially in-process for
debugging on a workstation.
Usage on SLURM:
uv run python examples/port_datasets/slurm_build_robocasa_composite_seen.py \
--output-dir=/scratch/${USER}/robocasa_composite_seen \
--hub-repo-id=${HF_USER}/robocasa_composite_seen \
--logs-dir=/scratch/${USER}/logs/robocasa \
--partition=cpu --push-to-hub
Prereq: uv sync --extra annotations (pulls datatrove)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Pepijn
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5bb2da4da6 |
fix(pi052): VQA target format = "label <loc><loc>" not "<loc><loc> label"
The trained model collapsed to spewing 40+ <loc> tokens for *every* prompt — subtask, memory, anything — because VQA targets were supervised to *start* with <loc>. With ~25% of all text samples beginning with a <loc> token, the LM head learned "Assistant: → <loc>" as a strong attractor; once one loc is emitted, autoregression chains the rest. Flip the format so every text target — subtask, memory, speech, AND VQA — starts with a regular word. The model still learns the <loc> vocabulary for the spatial portion of the answer, but loc can no longer be the first generation step out of a clean prompt. Examples: point : "green box <loc0162><loc0759>" bbox : "cube <loc0082>…<loc0409>" multi : "blue <locs> ; yellow <locs>" The runtime parser (parse_loc_answer) strips loc tokens and uses the remainder as label, so it's order-tolerant and works under either format. Old loc-first checkpoints still parse cleanly at inference; new training will use label-first. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Pepijn
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34269a5d78 |
fix(pi052): register PaliGemma <loc> tokens so they tokenize as single ids
THE bug behind the <loc>-salad. PaliGemma's vocab reserves ids [256000, 257023] for <locDDDD> detection / pointing tokens, but the stock AutoTokenizer does NOT match them on raw text — it BPE-splits <loc0162> into SEVEN pieces (<, loc, 0, 1, 6, 2, >). So a VQA target like "<loc0162><loc0759> green box<eos>" tokenized to 16 pieces, not 5, and training the LM head supervised those generic BPE pieces instead of one detection-vocab id. The piece logits got pumped up across ~25% of supervised positions; at inference they dominated every turn — even subtask prompts produced <loc>-salad followed by the actual answer. Register the 1024 <locDDDD> tokens via tokenizer.add_tokens once on load, in every path the policy uses: PI052TextTokenizerStep (training encode), _build_text_batch_pi052 (runtime encode), and select_message's default tokenizer (runtime decode). Verified empirically with the real PaliGemma tokenizer: VQA target now tokenizes to 5 ids matching the loc-vocab range (256162, 256759, ...) with correct offset_mapping. This unlocks PaliGemma's actual detection prior; <loc>-salad cannot recur because each <locDDDD> is a single class on the LM head, not a character sequence the head accidentally learns to extend. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Pepijn
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75507491bf |
fix(pi052): VQA <loc> conversion treats coords as 0-1000 normalized
Confirmed empirically on the published dataset: VQA bbox/keypoint coordinates are Qwen2.5-VL's 0–1000 normalized grounding output, NOT pixels. Scanning 8207 samples showed x and y both spanning 0..1000 with ~30% of values exceeding the camera's pixel dimensions (which is impossible if they were pixels). _vqa_answer_to_loc was dividing by the observation image's H/W, so e.g. point [742, 158] on a 640x480 wrist cam clamped x to <loc1023> (the far-right edge) instead of mapping to <loc0760> (~74% across). Fix: divide by 1000 — the actual Qwen scale. The conversion is now camera-resolution-independent, so _camera_image_shapes and the image_shapes plumbing through __call__ / _encode_messages / _messages_vqa_to_loc are dropped. Tests updated to the new signature and the 0–1000 round-trip. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Pepijn
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b7317b6c29 |
test(pi052): round-trip coverage for VQA <loc> conversion
Pins JSON pixel coords -> PaliGemma <loc> -> runtime parse back: the conversion preserves coordinate order (JSON x-first, <loc> y-first) and per-axis normalization, losing only <loc>-grid quantization. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Pepijn
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c026aed8f8 |
feat(pi052): train VQA spatial answers in PaliGemma <loc> format
Spatial VQA answers (bbox / keypoint) were trained as pixel-coordinate JSON, which fights PaliGemma's detection prior and leaks <loc>-token salad at inference. Convert them to PaliGemma's native <locNNNN> vocabulary instead so the LM head reuses that prior. Training side (text_processor_pi052.py): a target turn whose content parses as a bbox/keypoint answer is rewritten to <loc> text, using the camera frame's native (H, W) from the observation and the preceding image block. Non-spatial answers, subtask/memory targets and SmolVLA2 keep their JSON form — the dataset stays backbone-agnostic. Runtime side (smolvla2/inference/vqa.py): parse_vqa_answer detects <loc> answers (2 locs -> keypoint, 4 -> bbox), returning normalized [0,1] coords with a normalized flag; draw_vqa_overlay denormalizes against the chosen camera frame's pixel size. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Pepijn
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15f79b5e5e |
fix(pi052): supervise an EOS token at the end of each text target
PI052TextTokenizerStep masked text_labels over the assistant turn's *content only* — the trailing newline was excluded and no EOS token was ever a supervised label. So the LM head was never given a stop signal: at inference select_message decoded to max_new_tokens, producing the runaway subtask paragraphs and the "}"}"}-style VQA tails. _format_messages now appends the tokenizer's EOS to each supervised target turn and extends that turn's span to cover it, so the EOS lands in text_labels. _shifted_ce then trains "<last content token> -> EOS" and the model learns to terminate; select_message stops on it. Inference callers (the runtime's _build_text_batch_pi052) pass no target_indices / eos_token, so no EOS is baked into the prompt — the model generates it. Verified end-to-end with the PaliGemma tokenizer: the supervised span is `<content><eos>` and the trailing newline stays unsupervised. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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pepijn
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22c9c4905e |
fix(pi052): avoid dense CE over padded tokens
Select only supervised text and FAST action-code positions before cross-entropy to avoid full-vocabulary loss tensors over padded sequences. Co-authored-by: Cursor <cursoragent@cursor.com> |
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pepijn
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1750a87104 |
fix(pi052): handle batched rendered messages
Tokenize batched recipe outputs in PI052 so training batches with nested message lists do not crash before model forward. Co-authored-by: Cursor <cursoragent@cursor.com> |
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pepijn
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0e2dc1b76f |
fix(pi052): supervise only FAST action-code tokens
Mask the FAST auxiliary loss to discrete action-code tokens so wrapper formatting tokens do not affect action co-training. Co-authored-by: Cursor <cursoragent@cursor.com> |
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Pepijn
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426d48dbbf |
fix(pi052): port the smolvla2 text-head fixes to pi052
pi052 had the same text-CE collapse bug smolvla2 had — PaliGemma's embed_prefix flags the language block att=0, so make_att_2d_masks makes it fully bidirectional and the text cross-entropy degenerates into a copy task. Ported the three model-specific fixes: - _mark_target_span_causal: set att=1 on supervised target language positions so the text-CE is genuine causal next-token prediction. Applied in both _compute_all_losses_fused and _compute_text_and_fast_loss. - flow_loss_weight 10.0 -> 5.0: the paper's a=10 swamps the LM head once the flow-only low_level recipe fires often (matches SmolVLA2Config). - _flatten_say_tool_calls in the text tokenizer: serialize `say` tool calls into a <say>...</say> marker so the spoken reply is tokenized and supervised (PaliGemma's flat prompt has no structured calls, so they were dropped entirely). select_message needed no change: pi052's prefix is [images, language] with no trailing state token, so it already decodes from the last language token. Regression tests mirror the smolvla2 attention-masking + tool-call suite. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Pepijn
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26cb38a7d0 |
feat(smolvla2): startup task picker, /vlm mode toggle, interactive VQA overlay
Three additions to the SmolVLA2 interactive runtime: 1. Startup task picker — when no --task is given, the runtime lists the dataset's task strings as a numbered menu (plus a custom-task option) instead of silently waiting for the first stdin line. 2. Mode toggle — /action and /vlm slash commands flip a persistent run mode. /vlm pauses the whole action loop (HighLevelSubtaskFwd, LowLevelForward and DispatchAction gate on state["mode"]) and clears the action queue so the robot holds position; /action resumes it. The mode is shown in the state panel. 3. Interactive VQA — in /vlm mode a typed line is a VQA question. The new inference/vqa.py module asks which camera to ground on, runs the VLM on that single camera, and when the answer is a bbox/keypoint it draws the overlay, saves a PNG to ./vqa_overlays/ and auto-opens it. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Pepijn
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bfb8cfb432 |
fix(smolvla2): flatten say tool_calls into <say> marker before tokenizing
The chat tokenizer passed assistant `tool_calls` straight to `apply_chat_template`, which renders them as a structured JSON `<tool_call>` block — so the LM head was trained to emit JSON. But the inference parser `_split_plan_and_say` looks for a `<say>...</say>` marker, which the model never saw in training, so the `say` tool never fired at inference. `_flatten_say_tool_calls` is the missing training-time serializer (the one `_split_plan_and_say`'s docstring already assumed existed): it rewrites a `say` tool call into a `<say>...</say>` marker inside the content text before the chat template runs, so the template only tokenizes plain text and the supervised target span trains the model to emit exactly the marker the runtime parses back (Pi 0.5-style flat tool-call serialization). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Pepijn
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0d88eaf8eb |
test(smolvla2): attention masking of the language target span
Regression coverage for the text-CE collapse bug fixed in
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Haoming Song
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e99c55af4b |
feat(policies): add EO-1 model (#3403)
* feat(policies): add EO-1 model * chore(eo1): adjust policy_eo1_README.md to to avoid duplicate with eo1.mdx * chore(eo1): remove policy_eo1_README.md, link eo1.mdx in policy folder --------- Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> |
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Khalil Meftah
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8a3d64033f |
Reward models refactor (#3142)
* feat(rewards): add RewardModelConfig and PreTrainedRewardModel base classes * refactor(rewards): migrate Classifier from policies/sac/reward_model/ to rewards/classifier/ * refactor(rewards): migrate SARM from policies/sarm/ to rewards/sarm/ * refactor(rewards): add rewards/factory.py and remove reward model code from policies/factory.py * refactor(rewards): update imports and delete old reward model locations * test(rewards): add reward model tests and update existing test imports * fix(rewards): restore full Classifier and SARM implementations * test(rewards): restore missing CUDA and mixed precision classifier processor tests * refactor(lerobot_train.py): remove rabc specific configuration and replace it with a generic samplerweight class in lerobot_train * refactor(lerobot_train.py): add missing sampling weight script * linter + missing files * add testing for sampl weighter * revert some useless changes, improve typing * update docs * add automatic detection of the progress path * remove type exp * improve comment * fix: move rabc.py to rewards/sarm/ and update import paths * refactor(imports): update reward model imports to new module structure * refactor(imports): update reward model imports to reflect new module structure * refactor(imports): conditionally import pandas based on availability * feat(configs): add reward_model field to TrainPipelineConfig and Hub fields to RewardModelConfig * refactor(policies): remove reward model branches from policy factory and __init__ * refactor(rewards): expand __init__ facade and fix SARMConfig __post_init__ crash * feat(train): route reward model training through rewards/factory instead of policies/factory * refactor(train): streamline reward model training logic * fix(rewards): ensure FileNotFoundError is raised for missing config_file * refactor(train): update __get_path_fields__ to include reward_model for config loading * refactor(classifier): remove redundant input normalization in predict_reward method * fix(train): raise ValueError for non-trainable reward models in train function * refactor(pretrained_rm): add model card template * refactor(tests): reward models * refactor(sarm): update reset method and remove unused action prediction methods * refactor(wandb): differentiate tags for reward model and policy training in cfg_to_group function * fix(train): raise ValueError for PEFT usage in reward model training * refactor(rewards): enhance RewardModelConfig with device handling and delta indices properties --------- Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> |
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Steven Palma
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ca87ccd941 |
feat(rollout): decouple policy deployment from data recording with new lerobot-rollout CLI (#3413)
* feat(scripts): lerobot-rollout * fix(rollout) require dataset in dagger + use duration too * fix(docs): dagger num_episodes * test(rollout): fix expectations * fix(rollout): features check * fix(rollout): device and task propagation + feature pos + warn fps + move rename_map config * docs(rollout): edit rename_map instructions * chore(rollout): multiple minor improvements * chore(rollout): address coments + minor improvements * fix(rollout): enable default * fix(tests): default value RTCConfig * fix(rollout): robot_observation_processor and notify_observation at policy frequency instead of interpolator rate Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> * fix(rollout): prevent relativeactions with sync inference engine Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> * fix(rollout): rtc reanchor to non normalized state Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> * fix(rollout): fixing the episode length to use hwc (#3469) also reducing default length to 5 minutes * feat(rollout): go back to initial position is now a config * fix(rollout): properly propagating video_files_size_in_mb to lerobot_dataset (#3470) * chore(rollout): note about dagger correction stage * chore(docs): update comments and docstring * fix(test): move rtc relative out of rollout module * fix(rollout): address the review comments --------- Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Maxime Ellerbach <maxime.ellerbach@huggingface.co> |
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Steven Palma
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a8b72d9615 |
feat(dataset): 2x faster dataloader via parallel decode, uint8 transport, and persistent workers (#3406)
* feat(dataset): 2xfaster dataloader * fix(dataset): streaming return uint8 decode * fix(tests): adjust normalization step comparison * fix(dataset): with threadexecutor + False default * chore(dataset): make it a config * fix(test): account for uint8 in training path testing |
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Khalil Meftah
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f5c801fd34 | fix(test): add missing device placement in multi-task DiT tests (#3349) | ||
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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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919184d6f8 |
feat(envs): lazy env init + AsyncVectorEnv as default for n_envs > 1 (#3274)
* docs(benchmarks): add benchmark integration guide and standardize benchmark docs Add a comprehensive guide for adding new benchmarks to LeRobot, and refactor the existing LIBERO and Meta-World docs to follow the new standardized template. Made-with: Cursor * refactor(envs): move dispatch logic from factory into EnvConfig subclasses Replace hardcoded if/elif chains in factory.py with create_envs() and get_env_processors() methods on EnvConfig. New benchmarks now only need to register a config subclass — no factory.py edits required. Net -23 lines: factory.py shrinks from ~200 to ~70 lines of logic. Made-with: Cursor * docs(benchmarks): clean up adding-benchmarks guide for clarity Rewrite for simpler language, better structure, and easier navigation. Move quick-reference table to the top, fold eval explanation into architecture section, condense the doc template to a bulleted outline. Made-with: Cursor * fix link * fix task count * fix: enable SmolVLA eval on LIBERO with custom camera mappings - Thread camera_name_mapping from LiberoEnv config through to gym envs - Sync features_map with camera_name_mapping in LiberoEnv.__post_init__ - Fix render() to use first available camera instead of hardcoded "image" - Handle non-dict final_info in rollout by falling back to info["is_success"] - Add use_peft legacy field to SmolVLAConfig for checkpoint compat - Add defaults to GR00TN15Config init=False fields for transformers 5.3 Made-with: Cursor * fix: use direct AutoresetMode import for gymnasium compat Made-with: Cursor * fix: handle gymnasium < 1.0 without AutoresetMode Made-with: Cursor * refactor: revert policy changes, keep env-only camera mapping fixes - Revert GR00T N1.5 default_factory/default changes (transformers compat) - Revert SmolVLA use_peft legacy field - Apply ruff formatting fixes - camera_name_mapping stays entirely in env/eval layer (no policy changes) Made-with: Cursor * Update docs/source/env_processor.mdx Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co> Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> * feat(envs): lazy env init + AsyncVectorEnv as default for n_envs > 1 LiberoEnv and MetaworldEnv previously allocated GPU resources (EGL context, OpenGL framebuffer) in __init__, before AsyncVectorEnv's fork(). Worker processes inherited stale GPU handles, causing EGL_BAD_CONTEXT crashes on first render. Fix: defer OffScreenRenderEnv / MT1 construction to _ensure_env(), called on first reset() or step() inside the worker subprocess. Each worker creates its own clean context after fork(). Also fixes lerobot_eval.py:170 (add_envs_task TODO): replace with env.call("task") which works with both SyncVectorEnv and AsyncVectorEnv. AsyncVectorEnv is now the default for n_envs > 1; auto-downgraded to SyncVectorEnv when n_envs=1 (no benefit, less overhead). Expected speedup: ~15-20x for LIBERO Spatial with batch_size=50. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix: close envs between tasks to prevent worker process accumulation eval_policy_all never closed environments after each task completed, causing AsyncVectorEnv worker processes to accumulate (N_tasks × n_envs). This led to OOM, BrokenPipeError and EOFError on multi-task benchmarks. Also fixes: - AsyncVectorEnv compat in envs/utils.py (use get_attr/call instead of .envs) - Tuple task handling in tokenizer_processor and lerobot_eval - _LazyAsyncVectorEnv for deferred worker spawning in LIBERO Made-with: Cursor * fix(eval): use task_description instead of task for language conditioning env.call("task") returns the LIBERO task name with underscores (e.g. "pick_up_the_black_bowl_...") instead of the natural language description ("pick up the black bowl ..."). The VLM tokenizes these completely differently, causing 0.0 reward across all episodes. Made-with: Cursor * docs: update adding_benchmarks for async env changes - Replace add_envs_task reference with env.call("task_description") - Update use_async_envs default to True - Add note about lazy GPU init for AsyncVectorEnv compatibility Made-with: Cursor * feat(eval): batch_size=auto + faster env loading - batch_size=0 (default) auto-tunes based on CPU cores, capped by n_episodes and 64. Removes the need for users to guess the right value. The old batch_size > n_episodes error is replaced by silently clamping to n_episodes. - _LazyAsyncVectorEnv accepts pre-computed spaces so only one temp env is created per suite (not per task). For libero_spatial (10 tasks) this avoids 9 redundant LiberoEnv instantiations during env setup. Made-with: Cursor * docs: add evaluation guide and update benchmarks doc - New docs/source/evaluation.mdx covering lerobot-eval usage, batch_size auto-tuning, AsyncVectorEnv performance, tuning tips, output format, multi-task evaluation, and programmatic usage. - Add evaluation page to _toctree.yml under Benchmarks section. - Update adding_benchmarks.mdx to reference batch_size auto default and link to the evaluation guide. Made-with: Cursor * docs(evaluation): remove benchmark table, rename section header Made-with: Cursor * perf(eval): shared memory, observation passthrough, task prefetch - AsyncVectorEnv now uses shared_memory=True for zero-copy observation transfer - LiberoEnvConfig.gym_kwargs passes observation_height/width to the env - eval_policy_all prefetches next task's workers while current task runs Made-with: Cursor * style: ruff format Made-with: Cursor * chore: revert env_processor.mdx changes (not part of this PR) Made-with: Cursor * ci(benchmarks): add isolated integration tests for libero and metaworld Each benchmark gets its own Docker image (lerobot[libero] / lerobot[metaworld] only) so incompatible dep trees cannot collide. A 1-episode smoke eval runs per benchmark on GPU runners. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * ci(benchmarks): pin action hashes and use uv sync --locked Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * ci(benchmarks): trigger only on envs/ or lerobot_eval.py changes Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(ci): set LIBERO_DATA_FOLDER to bypass interactive stdin prompt libero/__init__.py calls input() to ask about a custom dataset path, which raises EOFError when stdin is closed inside Docker. Setting LIBERO_DATA_FOLDER skips the prompt entirely. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * docs(benchmarks): add CI smoke test step to adding_benchmarks guide Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(ci): pre-create libero config in Dockerfile to bypass stdin prompt libero/__init__.py calls input() when ~/.libero/config.yaml is missing. We write the config at image build time (without importing libero) so the prompt never fires at runtime. Also trigger CI on pyproject.toml changes. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(ci): use shell to create libero config instead of multiline python -c The multiline RUN python -c "..." was being parsed as Dockerfile instructions. Use printf to write ~/.libero/config.yaml directly. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(ci): point libero config to bundled package init_files The config was pointing to /tmp/libero_init which doesn't exist. Use importlib.util.find_spec to locate the hf-libero package directory and write paths to the actual bundled bddl_files/init_files/assets. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(ci): add smolvla extra to benchmark Dockerfiles num2words (required by SmolVLM processor) is declared in lerobot[smolvla], not lerobot[libero/metaworld]. Install both extras together. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(eval): render_frame covers _LazyAsyncVectorEnv isinstance(env, AsyncVectorEnv) silently skipped _LazyAsyncVectorEnv, causing video rendering to produce no frames on the default async path. Switch to hasattr(env, "call") so any async-compatible env (including _LazyAsyncVectorEnv) hits the call("render") branch. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * refactor(envs): remove unused _get_sub_env_attr helper _get_sub_env_attr was defined but never called anywhere in the codebase. _sub_env_has_attr (its sibling) is kept — it is actively used in utils.py. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * chore: apply prettier formatting to docs Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * docs(env_processor): remove deprecated add_envs_task from pipeline example add_envs_task is replaced by env.call("task_description") in this PR. Remove it from the pipeline walkthrough and renumber the steps (8→7). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * refactor(envs): remove __del__ from _LazyAsyncVectorEnv __del__ is unreliable as a cleanup mechanism. close() is already called explicitly in the eval loop's finally block, so the finalizer is redundant. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(eval): prefetch next task's workers after close to avoid GPU memory overlap Previously, next task's AsyncVectorEnv workers were spawned while the current task was still running, causing both tasks' GPU contexts to coexist. Moving the prefetch start into the finally block (after env.close()) ensures workers for task N+1 only spin up once task N has released GPU memory. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * refactor(envs): move _LazyAsyncVectorEnv to utils and apply to metaworld _LazyAsyncVectorEnv lived in libero.py but metaworld had the same OOM problem: all tasks' AsyncVectorEnv workers were spawned eagerly, wasting GPU memory for tasks not yet running. Move the class to envs/utils.py so both environments share it, then apply the same is_async + lazy wrapping pattern in create_metaworld_envs. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * chore: remove out-of-scope benchmark/CI/docs files from PR Benchmark CI workflow, Dockerfiles, benchmark docs, evaluation smoke-test doc, and dispatch tests belong in a separate PR. Scope this PR to the async env init changes only. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * chore: restore adding_benchmarks + test_dispatch, drop env_processor changes - Restore docs/source/adding_benchmarks.mdx (belongs in this PR) - Restore tests/envs/test_dispatch.py (belongs in this PR) - Revert docs/source/env_processor.mdx to main (out of scope for this PR) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * docs(adding_benchmarks): remove CI smoke test step (coming in separate PR) Step 7 (Dockerfile + benchmark_tests.yml CI job) and its table rows are out of scope for this PR. The CI infrastructure will be added on top in a follow-up PR. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * refactor(envs): remove unused add_envs_task Replaced by env.call("task_description") in lerobot_eval.py. No callers remain in the codebase. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * style: fix prettier formatting in env_processor.mdx Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(eval): catch AttributeError and NotImplementedError explicitly for task description Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(envs): use forkserver context and close envs in test to prevent deadlock AsyncVectorEnv with default fork context leaks worker processes between test_policy parametrized cases; subsequent env creation deadlocks because new forked workers inherit stale pipe FDs from previous test's leaked workers. - configs.py: pass context="forkserver" to AsyncVectorEnv (matches _LazyAsyncVectorEnv) - test_policies.py: call close_envs(envs) at end of test_policy to clean up workers Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(envs): default use_async_envs=False in create_envs and make_env Tests that call make_env(n_envs=2) without passing use_async_envs were getting AsyncVectorEnv, whose forked workers can't resolve gym namespaces registered at runtime. Default to False (sync) so existing tests pass. lerobot_eval.py explicitly passes cfg.eval.use_async_envs, so the CLI async behaviour (controlled by EvalConfig.use_async_envs) is unchanged. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> --------- Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co> Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> |
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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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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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Bryson Jones
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2e069b1c47 |
Feature/add multitask diffusion transformer policy implementation (#2545)
* Add multitask diffusion transformer policy Add multitask diffusion transformer policy * expand the observation encoder to support differnt size encoders for vision and text * add RoPE attention module as this is shown to help training dynamics and generation quality for DiTs * update readme and citations for multitask dit policy * remove dino vision encoder and simplify text and vision encoders by removing inheritance structure * adjust factory comment * update docstring for multitask dit policy processor file * simplify config for multitask dit by merging and flattening everything, then adding comments to denote where some parameters are only used for specific objectives * add references to the modeling file comments * merge all modules files into the main modeling file * add torch.no_grad decorators * split up select action return statement * remove redundant asserts * add tutorial to training with multi_task_dit * fix bugs when testing on hardware * remove environment state conditioning * update typo in test instruction comment * add processor tests to multitask dit tests * move policy to top of file * use constants for indexing into batches and remove env state references * remove the base classes since we don't need to be able to extend * fix nit formatting in generate actions fcn * reformat and clean up tutorial for multitask dit policy * add more descriptions and depth to multitask dit tutorial * note origins of each training objective * rename config param for multiple vision encoders * refactor code to perform task tokenization in the processor instead of in the modeling code for multitask dit * add multitask dit to toc for docs * add conditional transformers import to match all other policies that use transformers lib * add test handling for multitask dit when transformers isnt available * skip tests without transformers * remove cropping of images smaller than the crop size * add kwargs arg to multitask dit constructor * add wallx dep conflict management for multitask dit policy * use hyphens for cleanliness in pyproject.toml * add conflict management to pyproject toml for pi conflict for mtdp as well * update tests script to not use unnecessary uv sync call which resolves dependencies that do not need to run. This drastically reduces CI run time * revert fast tests edits * update docs and readme files, fixing some typos and adding multitask dit to readme * chore(dependencies): upgrade transformers + hggingface-hub + peft + scipy * chore(dependencies): bump pi0 family to transformers v5 * chore(dependencies): bump wall x to transformers v5 * chore(dependencies): bump gr00t to transformers v5 * chore(style): fix pre-commit * fix(policy): xvla forced_bos_token missing * test(rl): skip ci tests for resnet10 * Fix: full pi models support for transformer v5 (#2967) * fix(pi): remove loss truncation * fix(pi): remove state padding before tokenization * fix(pi): fix image padding value * fix from_pretrain * add transformer v5 changes * remove reference * more fixes * make it work * add support for rest of pi family * add pifast work * more changes * more changes * more cleanup * fix torch params * dtype fix * torch compile * embed mismatch fix * revert groot * more nit fixes * remove unused classes * more fixes * revert * nit * torch dtype warning fix * but back dynamic renaming * add tie embedding --------- Co-authored-by: Yufei Sun <skieyfly@gmail.com> * chore: fix XVLA in transformers v5 (#3006) * test(policies): enable wall x CI testing * style(test): pre-commit check * style(test): pre-commit --------- Signed-off-by: Bryson Jones <63133702+brysonjones@users.noreply.github.com> Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Steven Palma <imstevenpmwork@ieee.org> Co-authored-by: Jade Choghari <chogharijade@gmail.com> Co-authored-by: Yufei Sun <skieyfly@gmail.com> Co-authored-by: Steven Palma <steven.palma@huggingface.co> |
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Altman
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e64fa667c3 |
fix(vqbet): use in-place fill_ to avoid overwriting DDP GPU buffers with CPU tensors (#3128)
* fix(vqbet): use in-place fill_ to avoid overwriting DDP GPU buffers with CPU tensors
When VQ discretization phase completes, the code was overwriting
register_buffer('discretized') and register_buffer('freeze_codebook')
with torch.tensor(True), which is created on CPU. DDP then fails in
_sync_buffers() with: RuntimeError: No backend type associated with
device type cpu. Fix by updating the buffers in-place with .fill_(True)
so device and registration are preserved.
Made-with: Cursor
* test(vqbet): add regression test for in-place buffer update during discretization
Verifies that discretize() updates the 'discretized' and 'freeze_codebook'
registered buffers in-place (via fill_()) rather than replacing them with new
CPU tensors. The test checks data_ptr() identity and that the tensors remain
registered buffers after the call. This prevents regressions of the DDP fix.
Made-with: Cursor
* test(vqbet): add GPU regression test to verify buffers stay on CUDA after discretize()
Directly catches the original DDP failure mode: when buffers are replaced with
torch.tensor(True) they land on CPU, causing NCCL to raise 'No backend type
associated with device type cpu' in _sync_buffers(). The GPU test places the
model on cuda:0 and asserts both buffers remain on CUDA after discretization.
Made-with: Cursor
* test(vqbet): simplify to single device-check test in test_policies.py
Per reviewer feedback: remove the separate test file and replace the two
CPU/GPU tests (with data_ptr checks) with a single focused test in
tests/policies/test_policies.py that only asserts the registered buffers
remain on the model device after discretize(). Uses DEVICE from tests/utils.py
so it runs on whatever device the CI/user selects (cpu, cuda, mps).
Made-with: Cursor
* style: fix import order in test_policies.py to pass ruff/pre-commit checks
Made-with: Cursor
---------
Co-authored-by: Zhan DiJia <2476100824@example.com>
Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co>
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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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Steven Palma
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85de893fa7 |
fix(ci): skip HF log in (and tests) in forks and community PRs (#3097)
* fix(ci): skip HF log in (and tests) in forks and community PRs * chore(test): remove comment about test meant to be only run locally * fix(tests): no hf log in decorator for xvla * fix(test): no decorator in yield |
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Steven Palma
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e489ba24fc |
feat(dependencies): require Python 3.12+ as minimum version (#3023)
* feat(dependecies): upgrade to python3.12 * fix(test): processor regex message * fix(test): processor regex message * fix(dependecies): resolve all tags in python 3.12 * fix(dependecies): add more hints to faster resolve * chore(dependecies): remove cli tag huggingface-hub dep * refactor(policy): update eagle for python3.12 * chore(docs): update policy creation for python 3.12 * chore(test): skip failing tests in macos |
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Steven Palma
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f0d2b37beb |
chore(dependencies): bump transformers v5 (#2964)
* chore(dependencies): upgrade transformers + hggingface-hub + peft + scipy * chore(dependencies): bump pi0 family to transformers v5 * chore(dependencies): bump wall x to transformers v5 * chore(dependencies): bump gr00t to transformers v5 * chore(style): fix pre-commit * fix(policy): xvla forced_bos_token missing * test(rl): skip ci tests for resnet10 * Fix: full pi models support for transformer v5 (#2967) * fix(pi): remove loss truncation * fix(pi): remove state padding before tokenization * fix(pi): fix image padding value * fix from_pretrain * add transformer v5 changes * remove reference * more fixes * make it work * add support for rest of pi family * add pifast work * more changes * more changes * more cleanup * fix torch params * dtype fix * torch compile * embed mismatch fix * revert groot * more nit fixes * remove unused classes * more fixes * revert * nit * torch dtype warning fix * but back dynamic renaming * add tie embedding --------- Co-authored-by: Yufei Sun <skieyfly@gmail.com> * chore: fix XVLA in transformers v5 (#3006) * test(policies): enable wall x CI testing * style(test): pre-commit check * style(test): pre-commit * fix wall x for transformer v5 (#3008) * tv5 fix * various wall x fixes * Delete tests/policies/pi0_pi05/print_pi05_output_logits.py Signed-off-by: Jade Choghari <chogharijade@gmail.com> * sync modeling_florence2.py with chore/bump_transformers_v5 * more * more fixes * more * remove comment * more --------- Signed-off-by: Jade Choghari <chogharijade@gmail.com> * chore(dependencies): adjust dependencies versioning after transformers v5 (#3034) * chore(dependecies): adjust dependecies versioning after transformers v5 * fix(policies): remove deprecated input_embeds * fix(policies): dict _tied_weights_keys * chore(depedencies): common qwen-vl-utils * chore(dependencies): bump transformers to 5.2 * Fix policy testing for tv5 (#3032) * fix ci logger * other fix * fix mypy * change logits to torch2.10 * skip wallx| * remove logging --------- Co-authored-by: Steven Palma <imstevenpmwork@ieee.org> * feat(ci): log into HF to unblock some CI tests (#3007) * feat(ci): log into HF to unblock some CI tests * chore(ci): change hf call + secret name * fix(ci): temp fix for pi0 rtc test * test(policies): require_cuda for unblocked tests * test(policies): require_cuda wall_x * fic(tests): require_cuda outter most for pi0 * fix(test): return instead of yield --------- Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> * style(test): fix pre-commit * chore(deps): upgrade transformers (#3050) * chore(test): use lerobot model * fix(policies): change default action tokenizer for wall x * sample on cpu * Revert "Merge branch 'chore/bump_transformers_v5' of https://github.com/huggingface/lerobot into chore/bump_transformers_v5" This reverts commit |
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Michel Aractingi
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04cbf669cf |
fix(sac): make temperature a property to fix checkpoint resume bug (#2877)
* fix(sac): make temperature a property to fix checkpoint resume bug Temperature was stored as a plain float and not restored after loading a checkpoint, causing incorrect loss computations until update_temperature() was called. Changed to a property that always computes from log_alpha, ensuring correct behavior after checkpoint loading. * simplify docstrings |
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Jade Choghari
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1d86c9b7f2 | feat(policies): add autoregressive VLAs with tokenization PiFast (#2734) | ||
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Pepijn
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6106a8136c |
Fix invalid syntax (#2752)
* fix invalid syntax * also skip for torchdiffeq * fix patch for gpu tests |
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Tong Wu
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17c5a0774f |
feat: support wallx model (#2593)
* support wallx * fix bugs in flow * incorporate wallx model into lerobot * update the policy methods * reduce to least config and params & pass lerobot basic test * fixed dtype bugs * add wallx dependencies * update * remove flash-attn requirement && fix bug in inference and fast mode * fix bug for inference * add some small modifications * fix pre-commit errors * remove lerobot[wallx] * fix ci * fix precommit issues * fix: exclude wallx extra properly in CI workflows * fix: add uv conflicts for wallx transformers version * fix: peft test import * pre-commit * only export WallXConfig from wall_x package to avoid peft import in CI * remove torch dep * precommit * add import --------- Co-authored-by: vincentchen <chenlufang@x2robot.com> Co-authored-by: Geoffrey19 <sympathischmann35@gmail.com> Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Pepijn <pepijn@huggingface.co> |
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Pepijn
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f04958527e |
Add sarm (#2639)
* add initial modeling * make rewind pretrained policy * add annotation * small fix * add sarm * subtasks * fix spawn * fix rewind discrepancies * Add script to generate embedding for dataset (#2138) * Add generate and validate script * fix precommit * Improve generate embeddings function by using dataset tools (#2206) --------- Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> * cleanup * change order train log * print batch size * update sarm processor * add reward output * change expected features * add image validation * change validation * get state input from dataset stats * raise if no state key is found * pass stats * cleanup and refactor * add episode inddex to complementary data * add subtask init and detection * revert lerobot_train changes * pass dataset metadata to policy * change loadig subtasks * add small logging * fix progress conversion and adding initial frame * use large offset for initial frame (ugly) * Remove rewind, use clip tokenizer * add tests, implement formula 1,2 correctly and cleanup * use task from dataset, cleanup visualizer * simplify * simplify and cleanup code and move compute_temporal_proportions to utils * fix normalization in visualization * Fix visualization and change prompt * fix formatting * add visualize subtask annotations * use qwen thinking * try different prompt * format * update prompt * higher temp, long output * different settings * use instruct * show full resp * split message * Temp: increase tolerance dataset * Fix RA-BC (#2572) * Add next observation loading for RA-BC progress deltas * Compute weights based on temporal progress deltas instead of static rewards * Add hard-masking for negative progress deltas in weight computation * Feat/add dual head (#2582) * Add dual dense sparse head and annotation * Add docs * add dual to procesor * cleanup * change sampling in visualize and cleanup * remove validation * remove compile * Feat/test uniform (#2587) * test uniform * add different string for misaligned * Fix rewind and add tests * uncomment text implementation * run precommit * Add head mode for ra-bc * fix visalization of single task * add * return per sample loss * Fix RA_BC (#2602) * update rabc implementation * compute rabc beforehand * fix import * add only progress calulation * use precomputed progress * multi gpu processing * import * fix dataset meta data extraction * add logging * logging * log * progress per episode * split differently * move clip to gpu * pre decode frames for an episode * fix cuda initalization * fix import * multi processing * rename * fix import * fix * fix rabc * use last known progress if oob * use last known progress if oob * add misalignment loss with random embeddings * discard previous changes * add selection of models to docs for ra_bc * add transformers dep * extend tolerance * initial commit with new codebase * add tests * fix * remove temporal sampler * drop last frame for sampler * use original ref * some fixes * fix visualization * remove smoothing and fix order subtasks * add stride rabc computation * add push to hub * add explanation * add kappa expllaination * better rabc logging * feedback pr * remove dataset tolerance * revert dataset tool * revert dataset changes * add credit * run precommit * change path for generate ra_bc * fix type * include sarm in all in pyproject * fix precommit * lazy import matplotlib * lazy import qwen * remove rich console * skip if transformers is not installed? * run only when we have faker * place transformer lazy loading * Dont test if low transformer version * fix * increase transformer * increase as 4.57.0 is yanked * remove pi from all * go back --------- Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> Co-authored-by: s1lent4gnt <kmeftah.khalil@gmail.com> |
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Jade Choghari
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43b0f17eb9 |
feat(policies): Add X-VLA (#2405)
* first commit * more fixes * add franka action * update testing script * add changes * update files * logits matching * add imagenet as a norm type * logits matching atol1e-2 * more eval fixes * more changes * xvla works on libero * remove seed * more refactoring * more fixes * more changes * more changes * more fixes * migrate policy revert * major pre-commit cleanup * renaming * revert to self.transformer * refactor * new changes * clean * update libero * more changes * make it work * more changes: * remove imagenet dependency * style * more * more refactor * remove proprio * add loss * more * more * add freeze/unfreeze options * add testing * upgrade transformers version * update testing * add installation * remove .sh file * fix testing * silent linter in xvlatest * fix failing test * upgrade test, fix failing * fix testing * more fixes to testing * require cuda in tests * temp check * add xvla docs * fix styling * update libero doc * remove timm dep * add different dtype support * remove timm skip * remove white lines * Enhance X-VLA finetuning documentation with optimizer details (#2537) Added detailed instructions for implementing a custom optimizer and modifying parameter retrieval for X-VLA finetuning. Signed-off-by: Jinliang Zheng <54488861+2toinf@users.noreply.github.com> * fix style * iterate on review * iterate on cpilot * revert xvla dep * free up ci * test(xvla): remove main test (#2565) * Add xvla custom optim and dtype (#2567) * add custom optim * add custom optim * add auto mode * more changes * add identity to all * add auto * release * add docs * make image smaller docs * smaller image in doc * evan smaller image doc * finalize doc --------- Signed-off-by: Jinliang Zheng <54488861+2toinf@users.noreply.github.com> Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> Co-authored-by: Jinliang Zheng <54488861+2toinf@users.noreply.github.com> Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> Co-authored-by: Steven Palma <imstevenpmwork@ieee.org> |
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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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Steven Palma
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be46bdea8f |
feat(policies): add Nvidia Gr00t N1.5 model (#2292)
* feat(policies): add Nvidia Gr00t N1.5 model Co-authored-by: lbenhorin <lbenhorin@nvidia.com> Co-authored-by: Aravindh <aravindhs@nvidia.com> Co-authored-by: nv-sachdevkartik <ksachdev@nvidia.com> Co-authored-by: youliangt <youliangt@nvidia.com> Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Jade Choghari <chogharijade@gmail.com> * fix(docs): add groot to index Co-authored-by: sachdevkartik <sachdev.kartik25@gmail.com> --------- Co-authored-by: lbenhorin <lbenhorin@nvidia.com> Co-authored-by: Aravindh <aravindhs@nvidia.com> Co-authored-by: nv-sachdevkartik <ksachdev@nvidia.com> Co-authored-by: youliangt <youliangt@nvidia.com> Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Jade Choghari <chogharijade@gmail.com> Co-authored-by: sachdevkartik <sachdev.kartik25@gmail.com> |
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Jade Choghari
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271d92dcaa |
feat(sim): add metaworld env (#2088)
* add metaworld * smol update Signed-off-by: Jade Choghari <chogharijade@gmail.com> * update design * Update src/lerobot/envs/metaworld.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Signed-off-by: Jade Choghari <chogharijade@gmail.com> * update * small changes * iterate on review * small fix * small fix * add docs * update doc * add better gif * smol doc fix * updage gymnasium * add note * depreciate gym-xarm * more changes * update doc * comply with mypy * more fixes * update readme * precommit * update pusht * add pusht instead * changes * style * add changes * update * revert * update v2 * chore(envs): move metaworld config to its own file + remove comments + simplify _format_raw_obs (#2200) * update final changes --------- Signed-off-by: Jade Choghari <chogharijade@gmail.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: Steven Palma <imstevenpmwork@ieee.org> |
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Steven Palma
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6c28ef894a | chore(docs): add missing license headers (#2140) | ||
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Pepijn
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abde7be3b3 |
Add OpenPi, Pi0 and Pi0.5 (#1910)
* 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 |
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Adil Zouitine
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2d3a605b3c |
Revert feat(normalization): add validation for empty features in NormalizerProcessorStep and UnnormalizerProcessorStep (#2087)
Revert "feat(normalization): add validation for empty features in NormalizerProcessorStep and UnnormalizerProcessorStep (#2087)"
This reverts commit
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Adil Zouitine
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f173265354 |
feat(normalization): add validation for empty features in NormalizerProcessorStep and UnnormalizerProcessorStep (#2087)
* feat(normalization): add validation for empty features in NormalizerProcessorStep and UnnormalizerProcessorStep * refactor(normalization): streamline feature reconstruction logic in _NormalizationMixin * refactor(tests): remove unused preprocessor initialization in test_act_backbone_lr --------- Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> |
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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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c5b5955c5a | chore: replace hard-coded next values with constants throughout all the source code (#2056) | ||
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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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7cf04a5ec3 | chore: move constants to utils (#2016) | ||
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Jade Choghari
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2538472781 | feat(sim): Add Libero Env (#1984) |