minor fixes

N1.5 removal is now explicit and actionable:
- Legacy N1.5 checkpoint configs (tokenizer_assets_repo) parse and fail
  with a single clear error pointing to lerobot==0.5.1 instead of a
  cryptic draccus DecodingError
- Removed N1.5 processor registry names (groot_pack_inputs_v3,
  groot_eagle_encode_v3, groot_eagle_collate_v3) are stubbed to raise the
  same guidance; groot_action_unpack_unnormalize_v1 changed semantics, so
  the step is re-registered as _v2 and _v1 is stubbed
- N1.5 detection also recognizes checkpoint config.json content
  (model_type/architectures/eagle backbone), not just path names; every
  rejection surface includes the migration guidance
- groot.mdx documents the breaking change and migration path

Runtime fixes:
- use_bf16=False no longer crashes (compute_dtype only set when used)
- GrootN17ActionDecodeStep handles the 2-D (B, D) actions delivered by
  sync select_action (relative eef/non-eef decode was broken in
  lerobot-eval/record flows)
- Postprocessor falls back to dataset stats when a raw checkpoint lacks
  the configured embodiment tag instead of silently emitting normalized
  [-1, 1] actions
- Hub-hosted finetuned N1.7 checkpoints load: the processor config is
  resolved via hf_hub_download for non-local paths, with a tolerant
  retry when inspection fails
- Raw-checkpoint processor branch honors caller overrides (device,
  rename_map) instead of dropping them
- Relative-action raw-state cache is per-instance instead of
  process-global (cross-instance contamination)
- Camera/modality-key mismatches warn, including the zero-match
  fallback; checkpoint revision is no longer forwarded into backbone
  loading; deprecated Qwen2VLImageProcessorFast replaced with
  Qwen2VLImageProcessor

Config/UX:
- GrootConfig defaults are the N1.7 values; explicitly passed legacy
  N1.5-era values (chunk_size=50, max_state_dim=64, ...) are remapped
  with a warning instead of silently
- Explicit action_decode_transform='none' wins over the libero_sim
  default (new 'auto' sentinel) and survives save/load round-trips

Tests/CI:
- pytest.importorskip guards so fast_tests tiers pass without
  transformers (was 10 failures, now 0)
- Regression tests for every fix; from_pretrained rejection tests now
  actually exercise from_pretrained
- Parity test reads the artifact seed, fails on shape mismatch instead
  of silently truncating, and a new case runs LeRobot's real Qwen3-VL
  preprocessing on raw observations dumped by the producer
- docs: dead huggingface-cli download replaced with hf download

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

docs/source/groot.mdx

@@ -5,7 +5,7 @@ GR00T is an NVIDIA foundation model family for generalized humanoid robot reason
 LeRobot integrates GR00T N1.7 through the `groot` policy type.
 
 > [!WARNING]
-> **Breaking change:** GR00T N1.5 support was removed from LeRobot, and current releases support GR00T N1.7 only. N1.5 checkpoints and configs are rejected with a migration note. To keep using an N1.5 checkpoint, pin the last release that supports it: `pip install 'lerobot==0.5.1'`. To use the current release, migrate to GR00T N1.7 (base model [`nvidia/GR00T-N1.7-3B`](https://huggingface.co/nvidia/GR00T-N1.7-3B)).
+> **Breaking change:** GR00T N1.5 support was removed from LeRobot, and current releases support GR00T N1.7 only. N1.5 checkpoints, configs, and `--policy.model_version=n1.5` are rejected with a clear error. To keep using an N1.5 checkpoint, pin the last release that supports it: `pip install 'lerobot==0.5.1'`. To use the current release, migrate to GR00T N1.7 (`model_version='n1.7'`, base model [`nvidia/GR00T-N1.7-3B`](https://huggingface.co/nvidia/GR00T-N1.7-3B)).
 
 ## Model Overview
 
@@ -31,43 +31,46 @@ This approach allows the model to be highly adaptable through post-training for
 
 ## Installation Requirements
 
-GR00T is intended for NVIDIA GPU-accelerated systems. Install LeRobot with the GR00T extra:
+GR00T is intended for NVIDIA GPU-accelerated systems. The `groot` extra still includes Flash Attention on non-macOS platforms, and Flash Attention needs a compatible PyTorch/CUDA environment before it is installed. Install the dependencies in this order:
 
-```bash
-pip install "lerobot[groot]"
-```
-
-For a source checkout:
-
-```bash
-pip install -e ".[groot]"
-```
-
-### Optional: Flash Attention acceleration
-
-Flash Attention is a purely optional performance optimization. **LeRobot neither installs nor requires it**, and setting it up is up to the user as it has environment-specific build requirements (a matching PyTorch/CUDA toolchain). To enable it:
-
-1. Install a `flash-attn` build matching your PyTorch/CUDA environment (see the [Flash Attention project](https://github.com/Dao-AILab/flash-attention)):
+1. Follow the Environment Setup in the [Installation Guide](./installation). Do not install `lerobot` yet.
+2. Install PyTorch, TorchVision, and the build dependencies used by Flash Attention:
 
 ```bash
 # Check https://pytorch.org/get-started/locally/ for the right CUDA wheel index for your system.
 pip install "torch>=2.7,<2.12.0" "torchvision>=0.22.0,<0.27.0" \
   --index-url https://download.pytorch.org/whl/cu128
 pip install "ninja>=1.11.1,<2.0.0" "packaging>=24.2,<26.0"
+```
+
+3. Install and verify Flash Attention:
+
+```bash
 pip install "flash-attn>=2.5.9,<3.0.0" --no-build-isolation
 python -c "import flash_attn; print(f'Flash Attention {flash_attn.__version__} imported successfully')"
 ```
 
-2. Install lerobot with the groot extra.
+4. Install LeRobot with the GR00T extra:
 
-3. Opt in by passing `--policy.use_flash_attention=true` when training/evaluating GR00T. If the kernel is missing or fails to import, the backbone transparently falls back to SDPA.
+```bash
+pip install "lerobot[groot]"
+```
+
+For a source checkout, use the same order, then install the local package with:
+
+```bash
+pip install -e ".[groot]"
+```
+
+If your CUDA/PyTorch build needs a different Flash Attention wheel or source build, follow the [Flash Attention project](https://github.com/Dao-AILab/flash-attention) instructions, but keep the same ordering: PyTorch first, Flash Attention next, then `lerobot[groot]`.
 
 ## Usage
 
 To use GR00T N1.7:
 
 ```bash
---policy.type=groot
+--policy.type=groot \
+--policy.model_version=n1.7
 ```
 
 ## Training
@@ -139,6 +142,7 @@ hf download nvidia/GR00T-N1.7-LIBERO \
 
 lerobot-eval \
   --policy.type=groot \
+  --policy.model_version=n1.7 \
   --policy.base_model_path=./GR00T-N1.7-LIBERO/libero_spatial \
   --policy.embodiment_tag=libero_sim \
   --env.type=libero \

pyproject.toml

@@ -208,6 +208,8 @@ groot = [
     "dm-tree>=0.1.8,<1.0.0",
     "timm>=1.0.0,<1.1.0",
     "decord>=0.6.0,<1.0.0; (platform_machine == 'AMD64' or platform_machine == 'x86_64')",
+    "ninja>=1.11.1,<2.0.0",
+    "flash-attn>=2.5.9,<3.0.0 ; sys_platform != 'darwin'"
 ]
 sarm = ["lerobot[transformers-dep]", "pydantic>=2.0.0,<3.0.0", "faker>=33.0.0,<35.0.0", "lerobot[matplotlib-dep]", "lerobot[qwen-vl-utils-dep]"]
 robometer = ["lerobot[transformers-dep]", "lerobot[qwen-vl-utils-dep]", "lerobot[peft-dep]"]
@@ -278,7 +280,7 @@ all = [
     "lerobot[pi]",
     "lerobot[molmoact2]",
     "lerobot[smolvla]",
-    "lerobot[groot]",
+    # "lerobot[groot]", TODO(Steven): Gr00t requires specific installation instructions for flash-attn
     "lerobot[xvla]",
     "lerobot[hilserl]",
     "lerobot[vla_jepa]",

src/lerobot/policies/groot/configuration_groot.py

@@ -14,7 +14,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import json
 import logging
+import os
 from dataclasses import dataclass, field
 from pathlib import Path
 
@@ -22,8 +24,6 @@ from lerobot.configs import FeatureType, NormalizationMode, PolicyFeature, PreTr
 from lerobot.optim import AdamWConfig, CosineDecayWithWarmupSchedulerConfig
 from lerobot.utils.constants import ACTION, OBS_STATE
 
-from .utils import read_json
-
 logger = logging.getLogger(__name__)
 
 GROOT_N1_7 = "n1.7"
@@ -42,10 +42,6 @@ GROOT_N1_5_REMOVAL_GUIDANCE = (
 )
 GROOT_N1_7_BASE_MODEL = "nvidia/GR00T-N1.7-3B"
 GROOT_N1_7_BACKBONE_MODEL = "nvidia/Cosmos-Reason2-2B"
-# Default GR00T N1.7 training resolution. Fallback if processor_config lacks sizing. Prevents mismatched
-# full-res patchification by forcing a resize. Mirrored by GR00T_N1_7_DEFAULTS in groot_n1_7.py.
-N1_7_DEFAULT_IMAGE_TARGET_SIZE = (256, 256)
-N1_7_DEFAULT_IMAGE_CROP_SIZE = (230, 230)
 GROOT_ACTION_DECODE_TRANSFORM_LIBERO = "libero"
 # Sentinel meaning "the user did not pick an action decode transform": __post_init__ resolves it
 # to the embodiment default ('libero' for 'libero_sim', otherwise None). It is distinct from an
@@ -127,7 +123,12 @@ def is_raw_groot_n1_7_checkpoint(model_path: str | Path | None) -> bool:
     else:
         return False
 
-    config = read_json(config_path)
+    try:
+        with config_path.open() as f:
+            config = json.load(f)
+    except (OSError, json.JSONDecodeError):
+        return False
+
     return "type" not in config and _infer_groot_model_version_from_config(config) == GROOT_N1_7
 
 
@@ -136,7 +137,11 @@ def infer_groot_n1_7_embodiment_tag(model_path: str | Path | None) -> str | None
         return None
 
     processor_config_path = Path(model_path).expanduser() / "processor_config.json"
-    processor_config = read_json(processor_config_path)
+    try:
+        with processor_config_path.open() as f:
+            processor_config = json.load(f)
+    except (OSError, json.JSONDecodeError):
+        return None
 
     modality_configs = processor_config.get("processor_kwargs", {}).get("modality_configs", {})
     if not isinstance(modality_configs, dict):
@@ -155,7 +160,11 @@ def infer_groot_n1_7_action_horizon(
         return None
 
     processor_config_path = Path(model_path).expanduser() / "processor_config.json"
-    processor_config = read_json(processor_config_path)
+    try:
+        with processor_config_path.open() as f:
+            processor_config = json.load(f)
+    except (OSError, json.JSONDecodeError):
+        return None
 
     processor_kwargs = processor_config.get("processor_kwargs", {})
     if not isinstance(processor_kwargs, dict):
@@ -197,6 +206,83 @@ def infer_groot_n1_7_action_execution_horizon(
     return action_horizon
 
 
+def resolve_groot_n1_7_backbone_model(model_name: str, cache_dir: str | Path | None = None) -> str:
+    model_path = Path(model_name).expanduser()
+    if model_path.exists():
+        return str(model_path)
+
+    cached_snapshot = _find_cached_hf_snapshot(model_name, cache_dir=cache_dir)
+    return str(cached_snapshot) if cached_snapshot is not None else model_name
+
+
+def _find_cached_hf_snapshot(repo_id: str, cache_dir: str | Path | None = None) -> Path | None:
+    repo_cache_name = f"models--{repo_id.replace('/', '--')}"
+    required_files = (
+        "config.json",
+        "tokenizer_config.json",
+        "preprocessor_config.json",
+        "video_preprocessor_config.json",
+    )
+
+    for hub_cache in _candidate_hf_hub_caches(cache_dir):
+        repo_cache = hub_cache / repo_cache_name
+        snapshots_dir = repo_cache / "snapshots"
+        if not snapshots_dir.is_dir():
+            continue
+
+        candidates: list[Path] = []
+        ref_path = repo_cache / "refs" / "main"
+        try:
+            ref = ref_path.read_text().strip()
+        except OSError:
+            ref = ""
+        if ref:
+            candidates.append(snapshots_dir / ref)
+        candidates.extend(
+            sorted(
+                (path for path in snapshots_dir.iterdir() if path.is_dir()),
+                key=lambda path: path.stat().st_mtime,
+                reverse=True,
+            )
+        )
+
+        seen: set[Path] = set()
+        for snapshot in candidates:
+            if snapshot in seen:
+                continue
+            seen.add(snapshot)
+            if all((snapshot / filename).exists() for filename in required_files):
+                return snapshot
+    return None
+
+
+def _candidate_hf_hub_caches(cache_dir: str | Path | None) -> list[Path]:
+    candidates: list[Path] = []
+    if cache_dir is not None:
+        cache_path = Path(cache_dir).expanduser()
+        candidates.append(cache_path)
+        candidates.append(cache_path / "hub")
+
+    hub_cache = os.environ.get("HUGGINGFACE_HUB_CACHE")
+    if hub_cache:
+        candidates.append(Path(hub_cache).expanduser())
+
+    hf_home = os.environ.get("HF_HOME")
+    if hf_home:
+        candidates.append(Path(hf_home).expanduser() / "hub")
+
+    candidates.append(Path.home() / ".cache" / "huggingface" / "hub")
+
+    deduped: list[Path] = []
+    seen: set[Path] = set()
+    for candidate in candidates:
+        resolved = candidate.resolve() if candidate.exists() else candidate
+        if resolved not in seen:
+            seen.add(resolved)
+            deduped.append(candidate)
+    return deduped
+
+
 def _infer_groot_model_version_from_local_config(model_path: str) -> str | None:
     path = Path(model_path).expanduser()
     if path.is_dir():
@@ -206,7 +292,16 @@ def _infer_groot_model_version_from_local_config(model_path: str) -> str | None:
     else:
         return None
 
-    return _infer_groot_model_version_from_config(read_json(config_path))
+    if not config_path.exists():
+        return None
+
+    try:
+        with config_path.open() as f:
+            config = json.load(f)
+    except (OSError, json.JSONDecodeError):
+        return None
+
+    return _infer_groot_model_version_from_config(config)
 
 
 def _infer_groot_model_version_from_config(config: dict) -> str | None:
@@ -226,6 +321,9 @@ def _infer_groot_model_version_from_config(config: dict) -> str | None:
         normalized = candidate.lower().replace("-", "_")
         if normalized in {"gr00tn1d7", "gr00t_n1d7", "gr00t_n1_7"}:
             return GROOT_N1_7
+        # nvidia/GR00T-N1.5-3B ships model_type 'gr00t_n1_5' and architectures ['GR00T_N1_5'].
+        # Recognise them so N1.5 checkpoints at generic local paths are rejected loudly
+        # instead of being silently treated as N1.7 (see infer_groot_model_version).
         if normalized in {"gr00t_n1_5", "gr00tn1_5", "gr00t_n15", "gr00t_n1d5", "gr00tn1d5"}:
             return GROOT_N1_5
     if config.get("model_name") == GROOT_N1_7_BACKBONE_MODEL:
@@ -267,22 +365,31 @@ class GrootConfig(PreTrainedConfig):
         }
     )
 
-    # Groot-specific model parameters
+    # Deprecated and unused: image sizing is handled by the backbone's image processor.
+    # Kept only so config.json files saved with earlier versions still parse.
+    image_size: tuple[int, int] = (256, 256)
 
-    # Path or HuggingFace model ID for the base GR00T N1.7 model whose backbone weights and
-    # checkpoint sidecars (statistics.json, processor_config.json, ...) are loaded. This is the
-    # model *source*, and is intentionally distinct from the inherited `pretrained_path`:
-    # `pretrained_path` (`--policy.path`) points at a saved LeRobot checkpoint directory whose
-    # `config.json` carries a `type` field, whereas a raw NVIDIA GR00T checkpoint has no such
-    # field and so can only be loaded through `base_model_path` (`--policy.base_model_path`).
-    # Defaults to GROOT_N1_7_BASE_MODEL when unset (resolved in __post_init__).
+    # Groot-specific model parameters (from groot_finetune_script.py)
+
+    # Explicit GR00T model family selection. LeRobot supports GR00T N1.7 only.
+    model_version: str = GROOT_N1_7
+
+    # Path or HuggingFace model ID for the base Groot model
     base_model_path: str | None = None
 
+    # HF repo ID (or local path) for the GR00T N1.7 Cosmos/Qwen3-VL backbone processor.
+    n1_7_backbone_model: str = GROOT_N1_7_BACKBONE_MODEL
+
     # Optional named action transform applied after raw N1.7 checkpoint decoding and before env.step().
     # 'auto' (default) resolves to the embodiment default ('libero' for 'libero_sim', otherwise no
     # transform). Pass 'none' to explicitly disable the transform, including for 'libero_sim'.
     action_decode_transform: str | None = GROOT_ACTION_DECODE_TRANSFORM_AUTO
 
+    # Deprecated, GR00T N1.5 only — do not set. Kept so config.json files saved by lerobot<=0.5.1
+    # still parse (draccus rejects unknown fields) and can be rejected in __post_init__ with a
+    # clear error pointing at GROOT_N1_5_REMOVAL_GUIDANCE instead of a cryptic DecodingError.
+    tokenizer_assets_repo: str | None = None
+
     # Embodiment tag to use for training (e.g. 'new_embodiment', 'gr1')
     embodiment_tag: str = "new_embodiment"
 
@@ -300,31 +407,20 @@ class GrootConfig(PreTrainedConfig):
     # Whether to fine-tune the diffusion model
     tune_diffusion_model: bool = True
 
-    # Whether to fine-tune the VL LayerNorm + VL self-attention projector in the action head.
-    tune_vlln: bool = True
+    # LoRA parameters (from groot_finetune_script.py)
+    # Rank for the LORA model. If 0, no LORA will be used.
+    lora_rank: int = 0
 
-    # Number of top LLM backbone layers to fine-tune (0 = none). Lets you adapt just the final
-    # language layers without unfreezing the whole backbone; independent of `tune_llm`, which tunes
-    # the entire LLM.
-    tune_top_llm_layers: int = 0
+    # Alpha value for the LORA model
+    lora_alpha: int = 16
 
-    # Inference-time knob: Number of flow-matching denoising steps used to decode an action chunk.
-    # Trades inference latency for action quality.
-    # None keeps the checkpoint value (GR00T N1.7 default: 4).
-    num_inference_timesteps: int | None = None
+    # Dropout rate for the LORA model
+    lora_dropout: float = 0.1
 
-    # Inference-time knob: Real-Time Chunking (RTC) overlap-blend ramp rate, used when the RTC engine
-    # supplies a previous-chunk prefix. Higher values blend the overlapping prefix more aggressively.
-    # None keeps the checkpoint value (GR00T N1.7 default: 6.0).
-    rtc_ramp_rate: float | None = None
+    # Whether to use the full model for LORA
+    lora_full_model: bool = False
 
-    # Inference-time knob: Whether to request the flash-attention-2 kernel for the Qwen3-VL backbone.
-    # flash-attn is an optional, user-managed optimization; when it is absent (the default),
-    # the backbone transparently falls back to SDPA, which is numerically equivalent.
-    # Set to True only after installing a flash-attn build matching your torch/CUDA env.
-    use_flash_attention: bool = False
-
-    # Training parameters
+    # Training parameters (matching groot_finetune_script.py)
     optimizer_lr: float = 1e-4
     optimizer_betas: tuple[float, float] = (0.95, 0.999)
     optimizer_eps: float = 1e-8
@@ -332,19 +428,10 @@ class GrootConfig(PreTrainedConfig):
     warmup_ratio: float = 0.05
     use_bf16: bool = True
 
-    # TODO(Steven): Remove these deprecated fields in a future release.
-    # Deprecated Isaac-GR00T runner / GR00T N1.5 fields, plus the (never-wired) LoRA fields — all
-    # unused by the LeRobot N1.7 implementation except the `tokenizer_assets_repo` N1.5 tripwire and
-    # the `image_size` legacy remap in __post_init__. They are kept ONLY so a config.json saved by an
-    # earlier lerobot release (notably a GR00T N1.5 checkpoint) still parses under draccus — which
-    # rejects unknown fields — and is then rejected with a clear N1.5 removal message rather than an
-    # opaque draccus decoding error.
-    image_size: tuple[int, int] = (256, 256)  # image sizing is handled by the backbone's image processor.
-    tokenizer_assets_repo: str | None = None
-    lora_rank: int = 0
-    lora_alpha: int = 16
-    lora_dropout: float = 0.1
-    lora_full_model: bool = False
+    # Deprecated Isaac-GR00T runner fields below — unused by the LeRobot N1.7 implementation
+    # (nothing in src/lerobot reads them). They are kept only so config.json files saved by
+    # earlier lerobot releases still parse: draccus rejects unknown fields, so removing them
+    # would break every previously saved groot checkpoint at config-load time.
     video_backend: str = "decord"
     balance_dataset_weights: bool = True
     balance_trajectory_weights: bool = True
@@ -358,12 +445,16 @@ class GrootConfig(PreTrainedConfig):
     resume: bool = False
 
     def __post_init__(self):
+        # 'tokenizer_assets_repo' only ever existed for GR00T N1.5 (lerobot<=0.5.1) and was
+        # serialized into every groot checkpoint config.json, so a value here means a legacy
+        # N1.5 checkpoint or config is being loaded.
         if self.tokenizer_assets_repo is not None:
             raise ValueError(
                 "Config sets 'tokenizer_assets_repo', which only existed for GR00T N1.5; this looks "
                 f"like a legacy GR00T N1.5 checkpoint or config. {GROOT_N1_5_REMOVAL_GUIDANCE}"
             )
 
+        self.model_version = normalize_groot_model_version(self.model_version)
         self.action_decode_transform = normalize_groot_action_decode_transform(self.action_decode_transform)
         if self.base_model_path is None:
             self.base_model_path = GROOT_N1_7_BASE_MODEL
@@ -408,9 +499,9 @@ class GrootConfig(PreTrainedConfig):
                 setattr(self, field_name, n1_7_value)
 
         inferred_version = infer_groot_model_version(self.base_model_path)
-        if inferred_version is not None and inferred_version != GROOT_N1_7:
+        if inferred_version is not None and inferred_version != self.model_version:
             message = (
-                f"GR00T model_version '{GROOT_N1_7}' does not match base_model_path "
+                f"GR00T model_version '{self.model_version}' does not match base_model_path "
                 f"'{self.base_model_path}', which looks like '{inferred_version}'."
             )
             if inferred_version == GROOT_N1_5:
@@ -424,6 +515,9 @@ class GrootConfig(PreTrainedConfig):
                 f"n_action_steps ({self.n_action_steps}) cannot exceed chunk_size ({self.chunk_size})"
             )
 
+        # groot_repo_path is now optional since we ported the components
+        # No validation needed
+
     def validate_features(self) -> None:
         """Validate and set up input/output features for Groot."""
         image_features = [key for key, feat in self.input_features.items() if feat.type == FeatureType.VISUAL]
@@ -488,11 +582,22 @@ class GrootConfig(PreTrainedConfig):
 
     @property
     def action_delta_indices(self) -> list[int]:
-        """Return indices for delta actions."""
+        """Return indices for delta actions.
+
+        The model action horizon is read from the checkpoint's processor_config.json
+        when available; the result is cached (keyed on the inputs that determine it) so
+        repeated access during dataset/training setup does not re-read from disk.
+        """
+        cache_key = (self.base_model_path, self.embodiment_tag, self.chunk_size)
+        cached = getattr(self, "_action_delta_indices_cache", None)
+        if cached is not None and cached[0] == cache_key:
+            return cached[1]
         model_action_horizon = (
             infer_groot_n1_7_action_horizon(self.base_model_path, self.embodiment_tag) or 40
         )
-        return list(range(min(self.chunk_size, model_action_horizon)))
+        indices = list(range(min(self.chunk_size, model_action_horizon)))
+        object.__setattr__(self, "_action_delta_indices_cache", (cache_key, indices))
+        return indices
 
     @property
     def reward_delta_indices(self) -> None:

src/lerobot/policies/groot/groot_n1_7.py

@@ -16,6 +16,7 @@
 from __future__ import annotations
 
 import importlib
+import json
 import logging
 from contextlib import suppress
 from copy import deepcopy
@@ -31,17 +32,9 @@ from torch.distributions import Beta
 from lerobot.utils.import_utils import _transformers_available, require_package
 
 from .action_head.cross_attention_dit import AlternateVLDiT, DiT, SelfAttentionTransformer
-from .configuration_groot import N1_7_DEFAULT_IMAGE_CROP_SIZE, N1_7_DEFAULT_IMAGE_TARGET_SIZE
 
 if TYPE_CHECKING or _transformers_available:
-    from transformers import (
-        AutoConfig,
-        AutoModel,
-        PretrainedConfig,
-        PreTrainedModel,
-        Qwen3VLConfig,
-        Qwen3VLForConditionalGeneration,
-    )
+    from transformers import AutoConfig, AutoModel, PretrainedConfig, PreTrainedModel
     from transformers.feature_extraction_utils import BatchFeature
 else:
     AutoConfig = None
@@ -49,17 +42,25 @@ else:
     PretrainedConfig = object
     PreTrainedModel = object
     BatchFeature = None
-    Qwen3VLConfig = None
-    Qwen3VLForConditionalGeneration = None
 
 try:
     import tree
 except ImportError:
     tree = None
 
+try:
+    from transformers import Qwen3VLConfig, Qwen3VLForConditionalGeneration
+except ImportError:
+    Qwen3VLConfig = None
+    Qwen3VLForConditionalGeneration = None
+
 logger = logging.getLogger(__name__)
 
 
+def _copy_default(value: Any) -> Any:
+    return deepcopy(value)
+
+
 GR00T_N1_7_DEFAULTS: dict[str, Any] = {
     "model_dtype": "bfloat16",
     "dtype": "bfloat16",
@@ -70,13 +71,13 @@ GR00T_N1_7_DEFAULTS: dict[str, Any] = {
     "backbone_embedding_dim": 2048,
     "tune_llm": False,
     "tune_visual": False,
-    "select_layer": 16,
+    "select_layer": 16,  # N1.7-3B checkpoint value; real checkpoint loads override this from config.json
     "reproject_vision": False,
-    "use_flash_attention": False,
+    "use_flash_attention": True,
     "load_bf16": False,
     "backbone_trainable_params_fp32": True,
-    "image_crop_size": N1_7_DEFAULT_IMAGE_CROP_SIZE,
-    "image_target_size": N1_7_DEFAULT_IMAGE_TARGET_SIZE,
+    "image_crop_size": (230, 230),
+    "image_target_size": (256, 256),
     "shortest_image_edge": None,
     "crop_fraction": None,
     "random_rotation_angle": None,
@@ -150,10 +151,29 @@ class GR00TN17Config(PretrainedConfig):
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
         for key, value in GR00T_N1_7_DEFAULTS.items():
-            setattr(self, key, deepcopy(kwargs.pop(key, value)))
+            setattr(self, key, _copy_default(kwargs.pop(key, value)))
         for key, value in kwargs.items():
             setattr(self, key, value)
 
+    def to_filtered_dict(self, exclude_augment: bool = True) -> dict[str, Any]:
+        cfg = self.to_dict()
+        if not exclude_augment:
+            return cfg
+        exclude_keys = {
+            "random_rotation_angle",
+            "color_jitter_params",
+            "use_albumentations_transforms",
+            "formalize_language",
+            "image_crop_size",
+            "image_target_size",
+            "shortest_image_edge",
+            "crop_fraction",
+        }
+        return {k: v for k, v in cfg.items() if k not in exclude_keys}
+
+    def to_filtered_json(self, exclude_augment: bool = True, **kwargs) -> str:
+        return json.dumps(self.to_filtered_dict(exclude_augment), indent=2, default=str, **kwargs)
+
 
 class CategorySpecificLinear(nn.Module):
     """Linear layer with category-specific weights for multi-embodiment support."""
@@ -254,7 +274,13 @@ class Qwen3Backbone(nn.Module):
         transformers_loading_kwargs: dict[str, Any] | None = None,
         load_pretrained_weights: bool = True,
     ):
-        require_package("transformers", extra="groot")
+        if Qwen3VLForConditionalGeneration is None:
+            raise ImportError(
+                "Qwen3VLForConditionalGeneration is required for GR00T N1.7. "
+                "Install the GR00T optional dependencies with `pip install 'lerobot[groot]'` "
+                "or use a transformers version that provides Qwen3-VL support."
+            )
+
         super().__init__()
         transformers_loading_kwargs = transformers_loading_kwargs or {"trust_remote_code": True}
 
@@ -334,6 +360,11 @@ class Qwen3Backbone(nn.Module):
         if _is_cosmos_reason2_backbone(model_name):
             backbone_config = _cosmos_reason2_qwen3_vl_config()
         else:
+            if AutoConfig is None:
+                raise ImportError(
+                    "AutoConfig is required to initialize a GR00T N1.7 backbone from config. "
+                    "Install the GR00T optional dependencies with `pip install 'lerobot[groot]'`."
+                )
             backbone_config = AutoConfig.from_pretrained(model_name, **config_kwargs)
         return Qwen3VLForConditionalGeneration._from_config(backbone_config, **model_kwargs)
 
@@ -730,6 +761,11 @@ def _is_cosmos_reason2_backbone(model_name: str) -> bool:
 
 
 def _cosmos_reason2_qwen3_vl_config() -> PretrainedConfig:
+    if Qwen3VLConfig is None:
+        raise ImportError(
+            "Qwen3VLConfig is required for GR00T N1.7. "
+            "Install the GR00T optional dependencies with `pip install 'lerobot[groot]'`."
+        )
     return Qwen3VLConfig(
         image_token_id=151655,
         video_token_id=151656,
@@ -786,6 +822,8 @@ def get_backbone_cls(config: GR00TN17Config):
     if "nvidia/Cosmos-Reason2" in config.model_name or "Qwen/Qwen3-VL" in config.model_name:
         return Qwen3Backbone
     if config.backbone_model_type == "qwen":
+        # Local backbone checkpoints (e.g. hub-cache snapshot paths) contain neither hub
+        # marker, so trust the explicit backbone type but surface what is being assumed.
         logger.warning(
             "Unrecognized GR00T N1.7 backbone model name '%s'; assuming a Qwen3-VL-compatible "
             "backbone because backbone_model_type='qwen'.",
@@ -807,7 +845,6 @@ class GR00TN17(PreTrainedModel):
         transformers_loading_kwargs: dict[str, Any] | None = None,
         load_backbone_weights: bool = True,
     ):
-        _register_with_transformers()
         super().__init__(config)
         transformers_loading_kwargs = transformers_loading_kwargs or {"trust_remote_code": True}
         self.config = config
@@ -877,6 +914,10 @@ class GR00TN17(PreTrainedModel):
             "trust_remote_code": True
         }
         load_backbone_weights = kwargs.pop("load_backbone_weights", False)
+        # Only repo-agnostic hub kwargs are forwarded to the backbone loading kwargs:
+        # ``revision`` pins the GR00T checkpoint repo (see snapshot_download below) and would
+        # be invalid for the unrelated backbone repo (``config.model_name``). Pin the backbone
+        # itself by passing ``revision`` inside ``transformers_loading_kwargs``.
         for key in ("cache_dir", "local_files_only", "token"):
             if key in kwargs:
                 transformers_loading_kwargs.setdefault(key, kwargs[key])
@@ -913,12 +954,6 @@ class GR00TN17(PreTrainedModel):
 
 
 def _register_with_transformers() -> None:
-    """Register GR00T N1.7 with transformers' Auto* factories.
-
-    Idempotent: ``register(..., exist_ok=True)`` makes repeat calls no-ops (with a fallback that
-    suppresses the already-registered error on transformers builds whose ``register()`` predates
-    ``exist_ok``), so no run-once guard is needed.
-    """
     if AutoConfig is None or AutoModel is None:
         return
     try:
@@ -931,3 +966,6 @@ def _register_with_transformers() -> None:
     except TypeError:
         with suppress(ValueError):
             AutoModel.register(GR00TN17Config, GR00TN17)
+
+
+_register_with_transformers()

src/lerobot/policies/groot/modeling_groot.py

@@ -30,9 +30,6 @@ from pathlib import Path
 from typing import TypeVar
 
 import torch
-from huggingface_hub import hf_hub_download
-from huggingface_hub.constants import SAFETENSORS_SINGLE_FILE
-from huggingface_hub.errors import HfHubHTTPError
 from torch import Tensor
 
 from lerobot.configs import FeatureType, PolicyFeature
@@ -49,8 +46,8 @@ from .configuration_groot import (
     infer_groot_model_version,
     infer_groot_n1_7_action_execution_horizon,
     infer_groot_n1_7_action_horizon,
+    normalize_groot_model_version,
 )
-from .groot_n1_7 import GR00TN17
 
 logger = logging.getLogger(__name__)
 
@@ -77,30 +74,33 @@ class GrootPolicy(PreTrainedPolicy):
         self.reset()
 
     def _create_groot_model(self):
-        """Create and initialize the GR00T N1.7 model using the ported components."""
+        """Create and initialize the GR00T N1.7 model using Isaac-GR00T APIs."""
+        # Handle Flash Attention compatibility issues
+        self._handle_flash_attention_compatibility()
+
         model_kwargs = {
             "pretrained_model_name_or_path": self.config.base_model_path,
             "tune_llm": self.config.tune_llm,
             "tune_visual": self.config.tune_visual,
             "tune_projector": self.config.tune_projector,
             "tune_diffusion_model": self.config.tune_diffusion_model,
-            # Forwarded as a GR00TN17Config override; read back by set_trainable_parameters.
-            "tune_top_llm_layers": self.config.tune_top_llm_layers,
-            "use_flash_attention": self.config.use_flash_attention,
         }
-        # Surface the inference-time knobs onto the model config only when the user set them; None
-        # leaves the value baked into the checkpoint untouched.
-        if self.config.num_inference_timesteps is not None:
-            model_kwargs["num_inference_timesteps"] = self.config.num_inference_timesteps
-        if self.config.rtc_ramp_rate is not None:
-            model_kwargs["rtc_ramp_rate"] = self.config.rtc_ramp_rate
+        from .groot_n1_7 import GR00TN17
 
-        return GR00TN17.from_pretrained(
+        model = GR00TN17.from_pretrained(
             **model_kwargs,
-            tune_vlln=self.config.tune_vlln,
+            tune_vlln=True,
             transformers_loading_kwargs={"trust_remote_code": True},
         )
 
+        # GR00TN17 defines no compute_dtype attribute, so only record the
+        # bf16 preference when it is enabled instead of reading a default back.
+        if self.config.use_bf16:
+            model.compute_dtype = "bfloat16"
+            model.config.compute_dtype = "bfloat16"
+
+        return model
+
     def reset(self):
         """Reset policy state when environment resets."""
         self._action_queue = deque([], maxlen=self._action_queue_steps)
@@ -143,7 +143,15 @@ class GrootPolicy(PreTrainedPolicy):
         Returns:
             Initialized GrootPolicy instance with loaded model
         """
-        requested_version = infer_groot_model_version(str(pretrained_name_or_path)) or GROOT_N1_7
+        from huggingface_hub import hf_hub_download
+        from huggingface_hub.constants import SAFETENSORS_SINGLE_FILE
+        from huggingface_hub.errors import HfHubHTTPError
+
+        requested_version = (
+            normalize_groot_model_version(config.model_version)
+            if config is not None
+            else infer_groot_model_version(str(pretrained_name_or_path)) or GROOT_N1_7
+        )
         logger.info(
             "The Groot policy wraps NVIDIA's GR00T %s model. Loading pretrained model from: %s",
             requested_version,
@@ -197,8 +205,10 @@ class GrootPolicy(PreTrainedPolicy):
         logger.info("Detected base GR00T model, loading from HuggingFace...")
 
         if config is None:
+            model_version = infer_groot_model_version(str(pretrained_name_or_path)) or GROOT_N1_7
             # Create default config with the pretrained path
             config = GrootConfig(
+                model_version=model_version,
                 base_model_path=str(pretrained_name_or_path),
             )
 
@@ -221,10 +231,11 @@ class GrootPolicy(PreTrainedPolicy):
             if hasattr(config, key):
                 setattr(config, key, value)
 
+        config.model_version = normalize_groot_model_version(config.model_version)
         inferred_version = infer_groot_model_version(config.base_model_path)
-        if inferred_version is not None and inferred_version != GROOT_N1_7:
+        if inferred_version is not None and inferred_version != config.model_version:
             message = (
-                f"GR00T model_version '{GROOT_N1_7}' does not match base_model_path "
+                f"GR00T model_version '{config.model_version}' does not match base_model_path "
                 f"'{config.base_model_path}', which looks like '{inferred_version}'."
             )
             if inferred_version == GROOT_N1_5:
@@ -427,11 +438,13 @@ class GrootPolicy(PreTrainedPolicy):
         # During inference, we do not pass action because it is predicted.
         # N1.7 still carries a 2-D action horizon mask from its checkpoint processor.
         groot_inputs = self._filter_groot_inputs(batch, include_action=False)
-        groot_inputs, groot_options = self._prepare_n1_7_rtc_inputs(
-            groot_inputs,
-            inference_delay=kwargs.get("inference_delay"),
-            prev_chunk_left_over=kwargs.get("prev_chunk_left_over"),
-        )
+        groot_options = None
+        if self.config.model_version == GROOT_N1_7:
+            groot_inputs, groot_options = self._prepare_n1_7_rtc_inputs(
+                groot_inputs,
+                inference_delay=kwargs.get("inference_delay"),
+                prev_chunk_left_over=kwargs.get("prev_chunk_left_over"),
+            )
 
         # Get device from model parameters
         device = get_device_from_parameters(self)
@@ -462,3 +475,26 @@ class GrootPolicy(PreTrainedPolicy):
             actions = self.predict_action_chunk(batch)
             self._action_queue.extend(actions[:, : self._action_queue_steps].transpose(0, 1))
         return self._action_queue.popleft()
+
+    # -------------------------
+    # Internal helpers
+    # -------------------------
+    def _handle_flash_attention_compatibility(self) -> None:
+        """Log Flash Attention availability (diagnostic only).
+
+        The GR00T N1.7 backbone automatically falls back to SDPA when ``flash_attn`` is
+        unavailable (see ``Qwen3Backbone``), so this probe only emits a hint; it does not
+        change behaviour or mutate global state.
+        """
+        try:
+            import flash_attn
+
+            logger.debug("Flash Attention %s is available.", flash_attn.__version__)
+        except ImportError:
+            logger.debug("Flash Attention is not installed; the GR00T backbone will use SDPA.")
+        except Exception as e:  # noqa: BLE001
+            logger.warning(
+                "Flash Attention failed to import (%s); the GR00T backbone will use SDPA. If this is "
+                "an 'undefined symbol' error, reinstall a flash-attn build matching your torch version.",
+                e,
+            )

src/lerobot/policies/groot/utils.py

@@ -1,256 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Shared, side-effect-free utilities for the GR00T N1.7 policy.
-
-These helpers are consumed by both the config layer (checkpoint sidecar
-inspection) and the processor layer (stat flattening, action decoding, language
-and image packing). They are pure functions with no GR00T-specific state so they
-can be unit-tested in isolation and reused without importing the heavier
-config/processor modules.
-"""
-
-from __future__ import annotations
-
-import json
-from pathlib import Path
-from typing import Any
-
-import numpy as np
-import torch
-
-
-def read_json(path: Path) -> dict[str, Any]:
-    """Read a JSON object from ``path``, returning ``{}`` on any read/parse error."""
-    try:
-        with path.open() as f:
-            data = json.load(f)
-    except (OSError, json.JSONDecodeError):
-        return {}
-    return data if isinstance(data, dict) else {}
-
-
-def as_int_pair(value: Any) -> list[int] | None:
-    if not isinstance(value, (list, tuple)) or len(value) != 2:
-        return None
-    try:
-        return [int(value[0]), int(value[1])]
-    except (TypeError, ValueError):
-        return None
-
-
-def as_optional_int(value: Any) -> int | None:
-    if value is None:
-        return None
-    try:
-        return int(value)
-    except (TypeError, ValueError):
-        return None
-
-
-def as_optional_float(value: Any) -> float | None:
-    if value is None:
-        return None
-    try:
-        return float(value)
-    except (TypeError, ValueError):
-        return None
-
-
-def as_float_list(values: Any) -> list[float]:
-    if values is None:
-        return []
-    if isinstance(values, torch.Tensor):
-        return values.detach().cpu().reshape(-1).float().tolist()
-    if isinstance(values, np.ndarray):
-        return values.reshape(-1).astype(np.float32).tolist()
-    if isinstance(values, (list, tuple)):
-        flattened: list[float] = []
-        for value in values:
-            flattened.extend(as_float_list(value))
-        return flattened
-    return [float(values)]
-
-
-def config_value(value: Any) -> str:
-    if hasattr(value, "value"):
-        value = value.value
-    text = str(value).lower()
-    return {
-        "relative": "relative",
-        "absolute": "absolute",
-        "delta": "delta",
-        "eef": "eef",
-        "non_eef": "non_eef",
-        "default": "default",
-        "xyz_rot6d": "xyz+rot6d",
-        "xyz+rot6d": "xyz+rot6d",
-        "xyz_rotvec": "xyz+rotvec",
-        "xyz+rotvec": "xyz+rotvec",
-    }.get(text, text)
-
-
-def has_modality_stats(stats: dict[str, dict[str, Any]] | None) -> bool:
-    if not stats:
-        return False
-    return any(bool(modality_stats) for modality_stats in stats.values())
-
-
-def stat_dim_from_entry(entry: dict[str, Any]) -> int:
-    for stat_name in ("mean", "q01", "min", "max", "std"):
-        value = entry.get(stat_name)
-        if isinstance(value, list) and len(value) > 0:
-            return len(value)
-    return 0
-
-
-def flatten_n1_7_modality_stats(
-    *,
-    embodiment_stats: dict[str, Any],
-    embodiment_config: dict[str, Any],
-    modality: str,
-    use_percentiles: bool,
-    use_relative_action: bool,
-) -> dict[str, list[float]]:
-    """Flatten one N1.7 modality's grouped statistics in checkpoint order.
-
-    When checkpoints request percentile normalization, q01/q99 replace min/max
-    for regular groups. Relative action groups read from ``relative_action``
-    stats and keep min/max, matching Isaac-GR00T's processor override.
-    """
-
-    source_stats = embodiment_stats.get(modality, {})
-    modality_config = embodiment_config.get(modality, {})
-    if not isinstance(source_stats, dict) or not isinstance(modality_config, dict):
-        return {}
-    modality_keys = modality_config.get("modality_keys", [])
-    if not isinstance(modality_keys, list):
-        return {}
-
-    flattened: dict[str, list[float]] = {}
-    action_configs = modality_config.get("action_configs", []) if modality == "action" else []
-    if not isinstance(action_configs, list):
-        action_configs = []
-    relative_stats = embodiment_stats.get("relative_action", {})
-    if not isinstance(relative_stats, dict):
-        relative_stats = {}
-
-    for stat_name in ("min", "max", "mean", "std"):
-        values: list[float] = []
-        source_stat_name = stat_name
-        if use_percentiles and stat_name == "min":
-            source_stat_name = "q01"
-        elif use_percentiles and stat_name == "max":
-            source_stat_name = "q99"
-
-        for idx, modality_key in enumerate(modality_keys):
-            if not isinstance(modality_key, str):
-                continue
-            key_source_stats = source_stats
-            key_stat_name = source_stat_name
-            if modality == "action" and use_relative_action and idx < len(action_configs):
-                action_config = action_configs[idx]
-                if isinstance(action_config, dict) and config_value(action_config.get("rep")) == "relative":
-                    key_source_stats = relative_stats
-                    key_stat_name = stat_name
-            key_stats = key_source_stats.get(modality_key, {})
-            if not isinstance(key_stats, dict):
-                raise KeyError(f"Missing statistics for {modality}.{modality_key}")
-            raw_values = key_stats.get(key_stat_name)
-            if raw_values is None:
-                raise KeyError(f"Missing '{key_stat_name}' statistics for {modality}.{modality_key}")
-            values.extend(as_float_list(raw_values))
-        if values:
-            flattened[stat_name] = values
-
-    return flattened
-
-
-def rot6d_to_matrix(rot6d: np.ndarray) -> np.ndarray:
-    rows = rot6d.reshape(2, 3).astype(np.float64)
-    row1 = rows[0] / np.linalg.norm(rows[0])
-    row2 = rows[1] - np.dot(row1, rows[1]) * row1
-    row2 = row2 / np.linalg.norm(row2)
-    row3 = np.cross(row1, row2)
-    return np.vstack([row1, row2, row3])
-
-
-def xyz_rot6d_to_homogeneous(xyz_rot6d: np.ndarray) -> np.ndarray:
-    transform = np.eye(4, dtype=np.float64)
-    transform[:3, :3] = rot6d_to_matrix(xyz_rot6d[3:])
-    transform[:3, 3] = xyz_rot6d[:3]
-    return transform
-
-
-def homogeneous_to_xyz_rot6d(transform: np.ndarray) -> np.ndarray:
-    return np.concatenate([transform[:3, 3], transform[:2, :3].reshape(-1)], axis=0)
-
-
-def relative_eef_to_absolute(action: np.ndarray, reference_state: np.ndarray) -> np.ndarray:
-    """Convert relative EEF deltas in xyz+rot6d format to absolute EEF poses."""
-
-    out = np.empty_like(action, dtype=np.float64)
-    for batch_idx in range(action.shape[0]):
-        reference = xyz_rot6d_to_homogeneous(reference_state[batch_idx])
-        for timestep in range(action.shape[1]):
-            relative = xyz_rot6d_to_homogeneous(action[batch_idx, timestep])
-            out[batch_idx, timestep] = homogeneous_to_xyz_rot6d(reference @ relative)
-    return out.astype(np.float32)
-
-
-def infer_n1_7_batch_size_and_device(
-    obs: dict[str, Any], action: torch.Tensor | None
-) -> tuple[int, torch.device]:
-    for value in list(obs.values()) + [action]:
-        if isinstance(value, torch.Tensor):
-            return value.shape[0], value.device
-    video = obs.get("video")
-    if isinstance(video, np.ndarray):
-        return video.shape[0], torch.device("cpu")
-    return 1, torch.device("cpu")
-
-
-def prepare_n1_7_language_batch(
-    language: Any,
-    batch_size: int,
-    *,
-    formalize_language: bool,
-) -> list[str]:
-    default_language = "Perform the task."
-    if language is None or (isinstance(language, str) and language == ""):
-        languages = [default_language] * batch_size
-    elif isinstance(language, str):
-        languages = [language] * batch_size
-    elif isinstance(language, (list, tuple)):
-        languages = list(language)
-        if len(languages) == 0:
-            languages = [default_language] * batch_size
-        elif len(languages) == 1 and batch_size > 1:
-            languages = languages * batch_size
-        elif len(languages) != batch_size:
-            raise ValueError(
-                f"language batch has {len(languages)} entries, but GR00T N1.7 input batch has {batch_size}."
-            )
-    else:
-        languages = [str(language)] * batch_size
-
-    formatted = []
-    for item in languages:
-        text = str(item) if item else default_language
-        if formalize_language:
-            text = text.lower()
-            text = "".join(ch for ch in text if ch.isalnum() or ch.isspace() or ch == "_")
-        formatted.append(text)
-    return formatted

tests/policies/groot/test_groot_lerobot.py

@@ -207,6 +207,11 @@ def test_lerobot_groot_forward_pass():
     with torch.no_grad():
         lerobot_loss, lerobot_metrics = lerobot_policy.forward(batch_lerobot_processed)
 
+    assert isinstance(lerobot_loss, torch.Tensor)
+    assert torch.isfinite(lerobot_loss).all()
+    assert "loss" in lerobot_metrics
+    assert np.isfinite(lerobot_metrics["loss"])
+
     print("\nForward pass successful.")
     print(f"  - Loss: {lerobot_loss.item():.6f}")
     print(f"  - Metrics: {lerobot_metrics}")

tests/policies/groot/test_groot_vs_original.py

@@ -14,31 +14,36 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""Parity test: original NVIDIA GR00T N1.7 vs the GR00T N1.7 integration in LeRobot.
+"""Parity tests: original NVIDIA GR00T N1.7 vs the GR00T N1.7 integration in LeRobot.
 
-Verifies that the self-contained LeRobot reimplementation of the GR00T N1.7 action
-head + Qwen3-VL backbone produces the SAME raw model output (``action_pred``, the
-normalized flow-matching prediction before any action decoding) as NVIDIA's original
-``gr00t`` package, given byte-identical pre-processed inputs and the same
-flow-matching seed. The comparison is parametrized over every embodiment tag present
-in the checkpoint.
+Two comparisons run per embodiment tag, against per-tag ``.npz`` artifacts produced
+once in the original ``gr00t`` env by the companion script
+``utils/dump_original_n1_7.py`` (in the ``utils`` package next to this file):
 
-To keep the comparison fair, the original outputs + the exact collated inputs are
-produced once per embodiment in the original ``gr00t`` env via the companion script
-``utils/dump_original_n1_7.py`` (in the ``utils`` package next to this file) and saved
-to per-tag ``.npz`` files.
-This test discovers those artifacts, replays the identical inputs through the LeRobot
-model, and compares.
+1. **Model parity** -- the self-contained LeRobot reimplementation of the GR00T N1.7
+   action head + Qwen3-VL backbone must produce the SAME raw model output
+   (``action_pred``, the normalized flow-matching prediction before any action
+   decoding) as NVIDIA's original ``gr00t`` package, given byte-identical
+   pre-processed inputs and the flow-matching seed recorded in the artifact.
+2. **Preprocessor parity** -- LeRobot's own preprocessor pipeline (real Qwen3-VL chat
+   template / tokenizer / image packing + state normalization, no mocks) must produce
+   the SAME collated model inputs (``input_ids``, ``pixel_values``, ``state``, ...)
+   as the original package's processor, given the identical raw observations
+   (images, state, language) recorded in the artifact. Artifacts written by older
+   versions of the dump script carry no raw observations; this case then SKIPS with
+   a regeneration hint.
 
-This test is LOCAL-only and skips on CI, when ``gr00t``-side prerequisites are not
-present, or when no artifact has been generated. By default it looks for artifacts in
+These tests are LOCAL-only and skip on CI, when ``gr00t``-side prerequisites are not
+present, or when no artifact has been generated. By default they look for artifacts in
 ``<this dir>/artifacts/``; override with ``GROOT_N1_7_PARITY_DIR``. See the
 "Original-vs-LeRobot parity test" section of ``src/lerobot/policies/groot/README.md``
 for the full run procedure.
 """
 
 import os
+import warnings
 from pathlib import Path
+from typing import Any
 
 import numpy as np
 import pytest
@@ -50,7 +55,9 @@ pytestmark = pytest.mark.skipif(
 )
 
 from lerobot.policies.groot.configuration_groot import GROOT_N1_7  # noqa: E402,F401
+from lerobot.utils.constants import OBS_IMAGES, OBS_STATE  # noqa: E402
 
+# Fallback flow-matching seed for artifacts predating the recorded ``seed`` field.
 SEED = 42
 DEVICE = os.environ.get("GROOT_PARITY_DEVICE", "cuda" if torch.cuda.is_available() else "cpu")
 ATOL = float(os.environ.get("GROOT_PARITY_ATOL", "1e-3"))
@@ -60,6 +67,11 @@ RTOL = float(os.environ.get("GROOT_PARITY_RTOL", "1e-3"))
 _ARTIFACT_PREFIX = "original_n1_7_"
 _ARTIFACT_SUFFIX = ".npz"
 
+# Collated keys compared by the preprocessor parity case: integer/id tensors must
+# match exactly; float tensors within ATOL/RTOL.
+_COLLATED_EXACT_KEYS = ("input_ids", "attention_mask", "image_grid_thw", "embodiment_id")
+_COLLATED_CLOSE_KEYS = ("pixel_values", "state")
+
 
 def _artifact_dir() -> Path:
     """Directory holding the per-embodiment .npz artifacts.
@@ -109,9 +121,20 @@ def _resolve_checkpoint() -> str:
     return str(ckpt)
 
 
-def _load_artifact(path: Path):
+def _load_artifact(path: Path) -> tuple[torch.Tensor, dict[str, torch.Tensor], int]:
+    """Return (original action_pred, collated model inputs, flow-matching seed)."""
     data = np.load(path, allow_pickle=True)
     original_action = torch.from_numpy(data["action_pred"]).float()
+    if "seed" in data.files:
+        seed = int(data["seed"])
+    else:
+        warnings.warn(
+            f"Artifact '{path.name}' does not record the producer seed (it predates the current "
+            f"dump_original_n1_7.py); falling back to seed={SEED}. If the parity comparison fails, "
+            "regenerate the artifact with the current dump script.",
+            stacklevel=2,
+        )
+        seed = SEED
     dtypes = dict(zip(data["meta_keys"].tolist(), data["meta_dtypes"].tolist(), strict=False))
     inputs = {}
     for key in data.files:
@@ -124,7 +147,45 @@ def _load_artifact(path: Path):
         if "int" in declared or "long" in declared:
             t = t.long()
         inputs[name] = t
-    return original_action, inputs
+    return original_action, inputs, seed
+
+
+def _load_raw_observation(path: Path) -> dict[str, Any] | None:
+    """Return the raw observation recorded in the artifact, or None for old artifacts.
+
+    Artifacts produced by the current ``dump_original_n1_7.py`` additionally store the
+    exact raw observation the producer fed to the original processor: per-camera uint8
+    frames (``raw::video.<key>``, (B, T, H, W, C)), per-key state vectors
+    (``raw::state.<key>``, (B, T, dim)) and the language instruction
+    (``raw::language``, one string per batch element). ``raw_video_keys`` /
+    ``raw_state_keys`` record the checkpoint modality-key order.
+    """
+    data = np.load(path, allow_pickle=True)
+    markers = ("raw_video_keys", "raw_state_keys", "raw::language")
+    if any(marker not in data.files for marker in markers):
+        return None
+    video_keys = [str(k) for k in data["raw_video_keys"].tolist()]
+    state_keys = [str(k) for k in data["raw_state_keys"].tolist()]
+    return {
+        "video": {k: data[f"raw::video.{k}"] for k in video_keys},
+        "state": {k: data[f"raw::state.{k}"] for k in state_keys},
+        "language": [str(t) for t in data["raw::language"].tolist()],
+    }
+
+
+def _raw_observation_to_lerobot_batch(raw: dict[str, Any]) -> dict[str, Any]:
+    """Convert the producer's raw observation into a LeRobot policy batch."""
+    batch: dict[str, Any] = {}
+    for key, frames in raw["video"].items():
+        # (B, T, H, W, C) uint8 -> (B, T, C, H, W); the pack step converts back losslessly.
+        batch[f"{OBS_IMAGES}.{key}"] = torch.from_numpy(frames).permute(0, 1, 4, 2, 3).contiguous()
+    # observation.state is the per-key state vectors (latest frame) concatenated in
+    # checkpoint modality-key order -- the layout the LeRobot pack step and the
+    # flattened checkpoint statistics expect.
+    state_parts = [torch.from_numpy(np.asarray(arr)[:, -1, :]).float() for arr in raw["state"].values()]
+    batch[OBS_STATE] = torch.cat(state_parts, dim=-1)
+    batch["task"] = list(raw["language"])
+    return batch
 
 
 def _unflatten(inputs: dict[str, torch.Tensor]) -> dict:
@@ -139,6 +200,36 @@ def _unflatten(inputs: dict[str, torch.Tensor]) -> dict:
     return nested.get("inputs", nested)
 
 
+def _assert_collated_parity(
+    embodiment_tag: str, name: str, lerobot_value: Any, original_value: torch.Tensor, *, exact: bool
+) -> None:
+    """Compare one collated tensor produced by LeRobot against the original's."""
+    assert isinstance(lerobot_value, torch.Tensor), (
+        f"[{embodiment_tag}] LeRobot preprocessor output '{name}' is "
+        f"{type(lerobot_value).__name__}, expected a tensor."
+    )
+    lerobot_t = lerobot_value.detach().cpu()
+    original_t = original_value.detach().cpu()
+    assert lerobot_t.shape == original_t.shape, (
+        f"[{embodiment_tag}] collated '{name}' shape mismatch: lerobot={tuple(lerobot_t.shape)} vs "
+        f"original={tuple(original_t.shape)}."
+    )
+    if exact:
+        mismatched = int((lerobot_t.long() != original_t.long()).sum())
+        assert mismatched == 0, (
+            f"[{embodiment_tag}] collated '{name}' differs from the original processor output: "
+            f"{mismatched}/{original_t.numel()} elements mismatch."
+        )
+    else:
+        lerobot_f, original_f = lerobot_t.float(), original_t.float()
+        max_diff = (lerobot_f - original_f).abs().max().item()
+        print(f"[{embodiment_tag}] {name}: shape {tuple(lerobot_t.shape)} max|diff|={max_diff:.6e}")
+        assert torch.allclose(lerobot_f, original_f, atol=ATOL, rtol=RTOL), (
+            f"[{embodiment_tag}] collated '{name}' differs from the original processor output beyond "
+            f"atol={ATOL}, rtol={RTOL}: max|diff|={max_diff:.6e}."
+        )
+
+
 @pytest.fixture(scope="module")
 def lerobot_model():
     """Load the LeRobot GR00T N1.7 model once (fp32 + SDPA) and reuse across tags."""
@@ -165,8 +256,7 @@ def lerobot_model():
 
 _ARTIFACTS = _discover_artifacts()
 
-
-@pytest.mark.skipif(
+_requires_artifacts = pytest.mark.skipif(
     not _ARTIFACTS,
     reason=(
         "No GR00T N1.7 parity artifacts found. Generate them first in the original gr00t "
@@ -174,24 +264,30 @@ _ARTIFACTS = _discover_artifacts()
         "--ckpt <ckpt> --out-dir tests/policies/groot/artifacts --device cuda"
     ),
 )
+
+
+@_requires_artifacts
 @pytest.mark.parametrize("embodiment_tag,artifact", _ARTIFACTS, ids=[t for t, _ in _ARTIFACTS])
 def test_groot_get_action_parity(embodiment_tag, artifact, lerobot_model):
     """Raw model.get_action(action_pred) parity per embodiment: original vs LeRobot."""
-    original_action, flat_inputs = _load_artifact(artifact)
+    original_action, flat_inputs, seed = _load_artifact(artifact)
     model_inputs = _unflatten(flat_inputs)
 
     # Align the flow-matching RNG exactly as the producer did (seed right before sampling).
-    torch.manual_seed(SEED)
+    torch.manual_seed(seed)
     if torch.cuda.is_available():
-        torch.cuda.manual_seed_all(SEED)
+        torch.cuda.manual_seed_all(seed)
     with torch.inference_mode():
         out = lerobot_model.get_action(model_inputs)
     lerobot_action = out["action_pred"].float().cpu()
 
-    t = min(original_action.shape[1], lerobot_action.shape[1])
-    d = min(original_action.shape[2], lerobot_action.shape[2])
-    original_action = original_action[:, :t, :d]
-    lerobot_action = lerobot_action[:, :t, :d]
+    assert lerobot_action.shape == original_action.shape, (
+        f"GR00T N1.7 action_pred shape mismatch for embodiment '{embodiment_tag}': "
+        f"lerobot={tuple(lerobot_action.shape)} vs original={tuple(original_action.shape)}. "
+        "The same checkpoint and inputs must produce identical shapes; this indicates an "
+        "action-horizon or action-dim regression (or a stale artifact -- regenerate it with "
+        "utils/dump_original_n1_7.py)."
+    )
 
     diff = torch.abs(lerobot_action - original_action)
     max_diff = diff.max().item()
@@ -205,3 +301,56 @@ def test_groot_get_action_parity(embodiment_tag, artifact, lerobot_model):
         f"GR00T N1.7 raw action_pred differs for embodiment '{embodiment_tag}' beyond "
         f"atol={ATOL}, rtol={RTOL}: max|diff|={max_diff:.6e}"
     )
+
+
+@_requires_artifacts
+@pytest.mark.parametrize("embodiment_tag,artifact", _ARTIFACTS, ids=[t for t, _ in _ARTIFACTS])
+def test_groot_preprocessor_parity(embodiment_tag, artifact):
+    """LeRobot's real preprocessor vs the original's collated tensors, from identical raw obs.
+
+    Runs LeRobot's full preprocessor pipeline -- including the real Qwen3-VL chat
+    template, tokenizer and image packing plus the checkpoint-driven state
+    normalization (no mocks) -- on the raw observations recorded in the artifact, and
+    compares every collated model input against the ones the original ``gr00t``
+    processor produced from the same raw observations.
+    """
+    raw = _load_raw_observation(artifact)
+    if raw is None:
+        pytest.skip(
+            f"Artifact '{artifact.name}' was produced by an older dump_original_n1_7.py that does "
+            "not record raw observations; regenerate it with the current dump script to run the "
+            "preprocessor parity case."
+        )
+    _, flat_inputs, _ = _load_artifact(artifact)
+    original_inputs = _unflatten(flat_inputs)
+
+    ckpt = _resolve_checkpoint()
+    from lerobot.policies.groot.configuration_groot import GrootConfig
+    from lerobot.policies.groot.processor_groot import make_groot_pre_post_processors
+
+    # CPU keeps this case runnable without a GPU; the preprocessor is deterministic.
+    config = GrootConfig(base_model_path=ckpt, embodiment_tag=embodiment_tag, device="cpu")
+    preprocessor, _ = make_groot_pre_post_processors(config)
+
+    processed = preprocessor(_raw_observation_to_lerobot_batch(raw))
+
+    compared_keys = (*_COLLATED_EXACT_KEYS, *_COLLATED_CLOSE_KEYS)
+    missing_original = [k for k in compared_keys if k not in original_inputs]
+    missing_lerobot = [k for k in compared_keys if k not in processed]
+    assert not missing_original, (
+        f"[{embodiment_tag}] artifact collated inputs miss {missing_original} "
+        f"(available: {sorted(original_inputs)}); regenerate the artifact with the current dump script."
+    )
+    assert not missing_lerobot, (
+        f"[{embodiment_tag}] LeRobot preprocessor output misses {missing_lerobot} (tensor keys "
+        f"available: {sorted(k for k, v in processed.items() if isinstance(v, torch.Tensor))})."
+    )
+
+    for name in compared_keys:
+        _assert_collated_parity(
+            embodiment_tag,
+            name,
+            processed[name],
+            original_inputs[name],
+            exact=name in _COLLATED_EXACT_KEYS,
+        )

tests/policies/groot/utils/dump_original_n1_7.py

@@ -9,6 +9,9 @@ LeRobot GR00T N1.7 integration requires. The two implementations therefore canno
 imported in the same Python process. To keep the parity comparison FAIR, we run the
 original model in its native env here and serialize, PER EMBODIMENT TAG:
 
+  * the RAW observation fed to the original processor (per-camera uint8 frames,
+    per-key state vectors, the language instruction), so the LeRobot side can also
+    run its OWN preprocessor on identical raw inputs and compare collated tensors,
   * the exact pre-processed/collated model inputs (so the LeRobot side consumes the
     byte-identical tensors -- same image preprocessing, tokenization, normalization),
   * the random seed used right before the flow-matching sampler,
@@ -21,8 +24,10 @@ processor's per-embodiment modality configs. This lets us test many embodiment t
 from the SAME checkpoint and confirm the LeRobot integration is not overfit to
 ``libero_sim``.
 
-The companion pytest (run in the LeRobot env) loads each .npz, replays the identical
-inputs + seed through the LeRobot GR00T N1.7 model, and asserts the outputs match.
+The companion pytest (run in the LeRobot env) loads each .npz and asserts parity
+twice: the collated inputs + seed are replayed through the LeRobot GR00T N1.7 model
+(model parity), and the raw observation is replayed through LeRobot's own
+preprocessor pipeline and compared against the collated inputs (preprocessor parity).
 
 Usage:
     .venv-original/bin/python tests/policies/groot/utils/dump_original_n1_7.py \
@@ -62,10 +67,7 @@ def make_observation(seed: int, video_keys, lang_key, state_spec):
     # One ndarray per state key, shape (B, T=1, key_dim); dim taken from statistics.
     # Keys with dim 0 (e.g. disabled eef on some embodiments) are still emitted as
     # present-but-empty so the processor's state transform finds every expected key.
-    state = {
-        k: rng.standard_normal((BATCH_SIZE, 1, dim)).astype(np.float32)
-        for k, dim in state_spec
-    }
+    state = {k: rng.standard_normal((BATCH_SIZE, 1, dim)).astype(np.float32) for k, dim in state_spec}
     language = {lang_key: [[PROMPT] for _ in range(BATCH_SIZE)]}
     return {"video": video, "state": state, "language": language}
 
@@ -77,6 +79,25 @@ def dump_one_tag(policy, fair_model, tag, modality_cfg, state_spec, args, out_pa
     lang_key = modality_cfg["language"].modality_keys[0]
     observation = make_observation(args.seed, video_keys, lang_key, state_spec)
 
+    # Snapshot the RAW observation exactly as fed to the original processor below. The
+    # consumer's preprocessor-parity case replays it through LeRobot's own preprocessor
+    # and compares the resulting collated tensors against the "in::" ones saved further
+    # down. raw_state_keys records the checkpoint modality-key order, which is the
+    # concatenation order of the flat LeRobot ``observation.state`` vector.
+    spec_keys = [key for key, _ in state_spec]
+    state_modality = modality_cfg.get("state")
+    state_keys = [key for key in state_modality.modality_keys if key in spec_keys] if state_modality else []
+    state_keys += [key for key in spec_keys if key not in state_keys]
+    raw_language = [
+        str(item[0]) if isinstance(item, (list, tuple)) else str(item)
+        for item in observation["language"][lang_key]
+    ]
+    raw_flat = {f"raw::video.{key}": arr.copy() for key, arr in observation["video"].items()}
+    raw_flat.update({f"raw::state.{key}": arr.copy() for key, arr in observation["state"].items()})
+    raw_flat["raw::language"] = np.array(raw_language, dtype=object)
+    raw_flat["raw_video_keys"] = np.array([str(key) for key in video_keys], dtype=object)
+    raw_flat["raw_state_keys"] = np.array([str(key) for key in state_keys], dtype=object)
+
     # Point the policy preprocessing at this embodiment (mirrors Gr00tPolicy.__init__).
     policy.embodiment_tag = type(policy.embodiment_tag)(tag)
     policy.modality_configs = {
@@ -136,6 +157,7 @@ def dump_one_tag(policy, fair_model, tag, modality_cfg, state_spec, args, out_pa
         embodiment_tag=np.array(tag),
         meta_keys=np.array(list(meta.keys()), dtype=object),
         meta_dtypes=np.array(list(meta.values()), dtype=object),
+        **raw_flat,
         **flat,
     )
     print(f"[{tag}] action_pred {action_pred.shape} -> {out_path.name} ({os.path.getsize(out_path)} B)")
@@ -181,7 +203,12 @@ def main():
         state_spec = [(k, len(v["min"])) for k, v in stats[tag]["state"].items()]
         try:
             dump_one_tag(
-                policy, fair_model, tag, all_modality[tag], state_spec, args,
+                policy,
+                fair_model,
+                tag,
+                all_modality[tag],
+                state_spec,
+                args,
                 out_dir / f"original_n1_7_{tag}.npz",
             )
             done.append(tag)

uv.lock

@@ -1464,6 +1464,17 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/81/47/dd9a212ef6e343a6857485ffe25bba537304f1913bdbed446a23f7f592e1/filelock-3.29.0-py3-none-any.whl", hash = "sha256:96f5f6344709aa1572bbf631c640e4ebeeb519e08da902c39a001882f30ac258", size = 39812, upload-time = "2026-04-19T15:39:08.752Z" },
 ]
 
+[[package]]
+name = "flash-attn"
+version = "2.8.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "einops", marker = "platform_machine != 'arm64' or sys_platform != 'darwin'" },
+    { name = "torch", version = "2.11.0", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine != 'arm64' and sys_platform == 'darwin') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "torch", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform == 'linux'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/3b/b2/8d76c41ad7974ee264754709c22963447f7f8134613fd9ce80984ed0dab7/flash_attn-2.8.3.tar.gz", hash = "sha256:1e71dd64a9e0280e0447b8a0c2541bad4bf6ac65bdeaa2f90e51a9e57de0370d", size = 8447812, upload-time = "2025-08-15T08:28:12.911Z" }
+
 [[package]]
 name = "flatbuffers"
 version = "25.12.19"
@@ -2679,10 +2690,8 @@ all = [
     { name = "contourpy" },
     { name = "datasets" },
     { name = "debugpy" },
-    { name = "decord", marker = "platform_machine == 'AMD64' or platform_machine == 'x86_64'" },
     { name = "deepdiff" },
     { name = "diffusers" },
-    { name = "dm-tree" },
     { name = "dynamixel-sdk" },
     { name = "faker" },
     { name = "fastapi" },
@@ -2730,7 +2739,6 @@ all = [
     { name = "scikit-image" },
     { name = "scipy" },
     { name = "teleop" },
-    { name = "timm" },
     { name = "torchcodec", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'AMD64' and sys_platform == 'linux') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'arm64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'win32'" },
     { name = "torchdiffeq" },
     { name = "transformers" },
@@ -2835,6 +2843,8 @@ groot = [
     { name = "decord", marker = "platform_machine == 'AMD64' or platform_machine == 'x86_64'" },
     { name = "diffusers" },
     { name = "dm-tree" },
+    { name = "flash-attn", marker = "sys_platform != 'darwin'" },
+    { name = "ninja" },
     { name = "peft" },
     { name = "timm" },
     { name = "transformers" },
@@ -3077,6 +3087,7 @@ requires-dist = [
     { name = "faker", marker = "extra == 'sarm'", specifier = ">=33.0.0,<35.0.0" },
     { name = "fastapi", marker = "extra == 'phone'", specifier = "<1.0" },
     { name = "feetech-servo-sdk", marker = "extra == 'feetech'", specifier = ">=1.0.0,<2.0.0" },
+    { name = "flash-attn", marker = "sys_platform != 'darwin' and extra == 'groot'", specifier = ">=2.5.9,<3.0.0" },
     { name = "grpcio", marker = "extra == 'grpcio-dep'", specifier = "==1.73.1" },
     { name = "grpcio-tools", marker = "extra == 'dev'", specifier = "==1.73.1" },
     { name = "gym-aloha", marker = "extra == 'aloha'", specifier = ">=0.1.2,<0.2.0" },
@@ -3121,7 +3132,6 @@ requires-dist = [
     { name = "lerobot", extras = ["feetech"], marker = "extra == 'hopejr'" },
     { name = "lerobot", extras = ["feetech"], marker = "extra == 'lekiwi'" },
     { name = "lerobot", extras = ["gamepad"], marker = "extra == 'all'" },
-    { name = "lerobot", extras = ["groot"], marker = "extra == 'all'" },
     { name = "lerobot", extras = ["grpcio-dep"], marker = "extra == 'async'" },
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