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.
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).
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.
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.
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.
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.
- 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).
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.
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.
Align the MolmoAct2 implementation with lerobot codebase conventions:
- Rename hf_model/ to molmoact2_hf_model/
- Slim config: move all I/O and runtime logic to modeling
- Remove blanket from 8 vendored files, fix 66 lint issues
- Deduplicate _hf_token() and _resolve_checkpoint_location()
- Make huggingface_hub imports lazy
- Remove custom MolmoAct2CosineDecayWithWarmupSchedulerConfig, use base class
- Extract 13 static/classmethods from MolmoAct2Policy to free functions
- Replace print() with logger in vendored action_tokenizer
- Add module docstrings, class docstring, and key method docstrings
- Add module-level loggers to modeling and processor
- Fix docs: pip to uv install, deduplicate README symlink
- Remove shebangs from all files
* feat(train): FSDP checkpoint saving
* adding docs for FSDP
* adding a test for the fsdp checkpoint path
* cleanup
* fixing final upload to hub
* refactored initial implementation to use torch fsdp api and adding new tests
* first commit
* feat(policies): add VLA-JEPA
* feat(policies): add VLA-JEPA
* support vla_jepa
* (feat)policies: add VLA-JEPA
* linting
* adding deps to pyproject.toml
* updating uv lock
* adding guards to avoid needing transformers and diffusers for type checking and basic tests
* fixing action and state dim
* fix warnings with qwen processor kwargs
* fixing wm_loss not propagating
* adjusting obs steps, tublets size to match original implementation
* some more fixes to be closer to the original implem
* adding more tests to ensure good coverage
* align VLA-JEPA architecture with original checkpoint
- Remove stale `action_num_heads` / `action_attention_head_dim` config fields;
DiT head dimensions are now always derived from the preset (DiT-B/L/test).
- Add `num_target_vision_tokens` and `action_max_seq_len` config fields required
by the action head's future-token embedding and positional embedding tables.
- Fix default `qwen_model_name` to 2B (matches all released checkpoints).
- Rename `ActionEncoder` attrs w1/w2/w3 → layer1/layer2/layer3 to match
checkpoint key names; replace `nn.Sequential` decoder/state-encoder with
`_MLP2` (layer1/layer2 naming).
- Fix `VLAJEPAActionHead` to size ActionEncoder and StateEncoder at `inner_dim`
(DiT input width) rather than `action_hidden_size` (DiT output width).
- Rename `DiT.blocks` → `transformer_blocks` and `attn` → `attn1` to match
checkpoint; add alternating cross/self attention (even blocks cross-attend to
Qwen context, odd blocks self-attend).
- Add `DiT-test` preset for unit tests.
- Rewrite `ActionConditionedVideoPredictor` with explicit ViT-style blocks
(`_PredictorBlock` with fused qkv) to match checkpoint structure; rename
`encoder`/`norm`/`proj` → `predictor_blocks`/`predictor_norm`/`predictor_proj`.
* propagate action_is_pad masking through VLA-JEPA policy pipeline
Pass the `action_is_pad` tensor from the batch through to the action head
so padded timesteps are excluded from the flow-matching loss.
* update VLA-JEPA tests for arch changes and action_is_pad
- Switch conftest to use `action_model_type="DiT-test"` now that
`action_num_heads` / `action_attention_head_dim` have been removed.
- Add action_head tests covering fully-padded loss (zero) and equivalence
of action_is_pad=None vs all-zeros mask.
- Remove obsolete `test_native_to_lerobot_wm_only` test.
* add VLA-JEPA documentation
Covers architecture overview, pretrained checkpoints, config reference,
training/eval commands for LIBERO-10, and guidance on fine-tuning for
single-camera datasets.
* add one-shot script to convert ginwind/VLA-JEPA checkpoints to safetensors (will remove once migrated)
* make default params more aligned with paper and pretrained models
- adding possibility of freezing qwen backbone and world model
- added tests for weight loading
* trying out to re-init the action head to avoid pretraining dimension mismatch
* allow different state dim and action dim
* removing missleading future_action_window_size to just use chunk_size
* lots of changes to make existing weights work, need to massively refactor the pre and post processing
* refactoring into using pre and post processor
* pre-commit cleanup
* fixing doc defaults args
Signed-off-by: Maxime Ellerbach <maxime@ellerbach.net>
* adressing dtype zeros issue
* adding guard for diffusers
* fixing training and exal examples
* trying to close success rate gap
* fix qwen norm layer output libero eval is now as expected
* adding instructions for different embodiement + fixing some tests
* smol fix to avoid having default CPU device when training
* fixing misconception about multiview / singleview handling
* removing conversion script
* adding licences
* adding .mdx docs and shortening polivy_vla_jepa_README.md
* removing useless pre-processor
* cleanup
* removing swish in favor of silu
* adding configuration gripper index and threshold
* fixing simlink
---------
Signed-off-by: Maxime Ellerbach <maxime@ellerbach.net>
Co-authored-by: ginwind <ginwind@mail.ustc.edu.cn>
* chore(gr00t): sync with #3606 for fixing gr00t config crash
* fix(pi0&pi05): fix graph break caused by deepcopy of past_key_values in sample_actions
* fix(pi0&pi05): fix frequent recompile caused by compute_layer_complete
* feat(test): add compile test and benchamrk for pi0 and pi05
* feat(test): add comprehensive testing for pi0 and pi05. Including processor, forward, sample action, etc.
* refactor: RL stack refactoring — RLAlgorithm, RLTrainer, DataMixer, and SAC restructuring
* chore: clarify torch.compile disabled note in SACAlgorithm
* fix(teleop): keyboard EE teleop not registering special keys and losing intervention state
Fixes#2345
Co-authored-by: jpizarrom <jpizarrom@gmail.com>
* fix: remove leftover normalization calls from reward classifier predict_reward
Fixes#2355
* fix: add thread synchronization to ReplayBuffer to prevent race condition between add() and sample()
* refactor: update SACAlgorithm to pass action_dim to _init_critics and fix encoder reference
* perf: remove redundant CPU→GPU→CPU transition move in learner
* Fix: add kwargs in reward classifier __init__()
* fix: include IS_INTERVENTION in complementary_info sent to learner for offline replay buffer
* fix: add try/finally to control_loop to ensure image writer cleanup on exit
* fix: use string key for IS_INTERVENTION in complementary_info to avoid torch.load serialization error
* fix: skip tests that require grpc if not available
* fix(tests): ensure tensor stats comparison accounts for reshaping in normalization tests
* fix(tests): skip tests that require grpc if not available
* refactor(rl): expose public API in rl/__init__ and use relative imports in sub-packages
* fix(config): update vision encoder model name to lerobot/resnet10
* fix(sac): clarify torch.compile status
* refactor(rl): update shutdown_event type hints from 'any' to 'Any' for consistency and clarity
* refactor(sac): simplify optimizer return structure
* perf(rl): use async iterators in OnlineOfflineMixer.get_iterator
* refactor(sac): decouple algorithm hyperparameters from policy config
* update losses names in tests
* fix docstring
* remove unused type alias
* fix test for flat dict structure
* refactor(policies): rename policies/sac → policies/gaussian_actor
* refactor(rl/sac): consolidate hyperparameter ownership and clean up discrete critic
* perf(observation_processor): add CUDA support for image processing
* fix(rl): correctly wire HIL-SERL gripper penalty through processor pipeline
(cherry picked from commit 9c2af818ff)
* fix(rl): add time limit processor to environment pipeline
(cherry picked from commit cd105f65cb)
* fix(rl): clarify discrete gripper action mapping in GripperVelocityToJoint for SO100
(cherry picked from commit 494f469a2b)
* fix(rl): update neutral gripper action
(cherry picked from commit 9c9064e5be)
* fix(rl): merge environment and action-processor info in transition processing
(cherry picked from commit 30e1886b64)
* fix(rl): mirror gym_manipulator in actor
(cherry picked from commit d2a046dfc5)
* fix(rl): postprocess action in actor
(cherry picked from commit c2556439e5)
* fix(rl): improve action processing for discrete and continuous actions
(cherry picked from commit f887ab3f6a)
* fix(rl): enhance intervention handling in actor and learner
(cherry picked from commit ef8bfffbd7)
* Revert "perf(observation_processor): add CUDA support for image processing"
This reverts commit 38b88c414c.
* refactor(rl): make algorithm a nested config so all SAC hyperparameters are JSON-addressable
* refactor(rl): add make_algorithm_config function for RLAlgorithmConfig instantiation
* refactor(rl): add type property to RLAlgorithmConfig for better clarity
* refactor(rl): make RLAlgorithmConfig an abstract base class for better extensibility
* refactor(tests): remove grpc import checks from test files for cleaner code
* fix(tests): gate RL tests on the `datasets` extra
* refactor: simplify docstrings for clarity and conciseness across multiple files
* fix(rl): update gripper position key and handle action absence during reset
* fix(rl): record pre-step observation so (obs, action, next.reward) align in gym_manipulator dataset
* refactor: clean up import statements
* chore: address reviewer comments
* chore: improve visual stats reshaping logic and update docstring for clarity
* refactor: enforce mandatory config_class and name attributes in RLAlgorithm
* refactor: implement NotImplementedError for abstract methods in RLAlgorithm and DataMixer
* refactor: replace build_algorithm with make_algorithm for SACAlgorithmConfig and update related tests
* refactor: add require_package calls for grpcio and gym-hil in relevant modules
* refactor(rl): move grpcio guards to runtime entry points
* feat(rl): consolidate HIL-SERL checkpoint into HF-style components
Make `RLAlgorithmConfig` and `RLAlgorithm` `HubMixin`s, add abstract
`state_dict()` / `load_state_dict()` for critic ensemble, target nets
and `log_alpha`, and persist them as a sibling `algorithm/` component
next to `pretrained_model/`. Replace the pickled `training_state.pt`
with an enriched `training_step.json` carrying `step` and
`interaction_step`, so resume restores actor + critics + target nets +
temperature + optimizers + RNG + counters from HF-standard files.
* refactor(rl): move actor weight-sync wire format from policy to algorithm
* refactor(rl): update type hints for learner and actor functions
* refactor(rl): hoist grpcio guard to module top in actor/learner
* chore(rl): manage import pattern in actor (#3564)
* chore(rl): manage import pattern in actor
* chore(rl): optional grpc imports in learner; quote grpc ServicerContext types
---------
Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co>
* update uv.lock
* chore(doc): update doc
---------
Co-authored-by: jpizarrom <jpizarrom@gmail.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* 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>
* 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>
johnnynunez