test(groot): add preprocessor-parity case to the original-vs-LeRobot test

The producer (dump_original_n1_7.py) now records the raw observation
(per-camera frames, state vectors, language) and the flow-matching seed in each
.npz artifact. The consumer gains a second comparison per embodiment tag:

- Preprocessor parity: LeRobot's full preprocessor pipeline (real Qwen3-VL chat
  template / tokenizer / image packing + checkpoint-driven state normalization,
  no mocks) runs on the recorded raw observations and its collated tensors are
  compared against the original processor's. Old artifacts without raw fields
  skip with a regeneration hint.
- Model parity now reads the recorded seed and fails on shape mismatch instead
  of silently truncating to the common horizon/dim.

Local-only; skips on CI, when gr00t prerequisites are absent, or when no
artifact has been generated.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

docs/source/groot.mdx

@@ -4,9 +4,6 @@ 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)).
-
 ## Model Overview
 
 GR00T N1.7 uses a Cosmos-Reason2/Qwen3-VL backbone and provides checkpoints for SimplerEnv, DROID, and LIBERO.
@@ -31,43 +28,46 @@ 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
@@ -133,12 +133,13 @@ Replace the `XX` placeholders with final eval artifacts before merge.
 Download the suite checkpoint locally, then point `--policy.base_model_path` at the downloaded subdirectory. `--policy.path` is reserved for LeRobot checkpoints that contain a LeRobot `config.json` with a `type` field.
 
 ```bash
-hf download nvidia/GR00T-N1.7-LIBERO \
+huggingface-cli download nvidia/GR00T-N1.7-LIBERO \
   --include "libero_spatial/*" \
   --local-dir ./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 \

docs/source/policy_groot_README.md

@@ -1,13 +1,6 @@
 ## Research Paper
 
-GR00T N1 technical report (covers the GR00T N1.x family, including N1.7): https://arxiv.org/abs/2503.14734
-
-GR00T N1.7 model card: https://huggingface.co/nvidia/GR00T-N1.7-3B
-
-GR00T N1.5 research page (earlier version): https://research.nvidia.com/labs/gear/gr00t-n1_5/
-
-> GR00T N1.5 support was removed from LeRobot; the last release supporting it is `lerobot==0.5.1`.
-> Current releases support GR00T N1.7 only.
+Paper: https://research.nvidia.com/labs/gear/gr00t-n1_5/
 
 ## Repository
 
@@ -38,22 +31,12 @@ Hugging Face Models:
 
 ## Original-vs-LeRobot parity test
 
-`tests/policies/groot/test_groot_vs_original.py` verifies this LeRobot
+`tests/policies/groot/test_groot_vs_original.py` verifies that this LeRobot
 reimplementation of GR00T N1.7 (Qwen3-VL backbone + flow-matching action head)
-against NVIDIA's original `gr00t` package with two comparisons, each parametrized
-over every embodiment tag present in the checkpoint:
-
-1. **Model parity** — given byte-identical pre-processed inputs and the same
-   flow-matching seed (recorded in each artifact), both implementations must produce
-   the **same raw model output** (`get_action(...)["action_pred"]`, the normalized
-   flow-matching prediction). Output shapes must match exactly; any action-horizon
-   or action-dim mismatch fails the test.
-2. **Preprocessor parity** — given the identical raw observations (per-camera
-   frames, state vectors, language instruction), LeRobot's own preprocessor pipeline
-   (real Qwen3-VL chat template / tokenizer / image packing + checkpoint-driven
-   state normalization, no mocks) must produce the **same collated model inputs**
-   (`input_ids`, `attention_mask`, `pixel_values`, `image_grid_thw`, `state`,
-   `embodiment_id`) as the original package's processor.
+produces the **same raw model output** (`get_action(...)["action_pred"]`, the
+normalized flow-matching prediction) as NVIDIA's original `gr00t` package, given
+byte-identical pre-processed inputs and the same flow-matching seed. It is
+parametrized over every embodiment tag present in the checkpoint.
 
 ### Why two environments
 
@@ -65,37 +48,25 @@ is itself a defaulted dataclass, so the original config dataclasses fail to impo
 
 So the test uses a **producer / consumer** split across two venvs:
 
-1. **Producer** — `tests/policies/groot/utils/dump_original_n1_7.py`, run in the _original_
+1. **Producer** — `tests/policies/groot/utils/dump_original_n1_7.py`, run in the *original*
    gr00t venv. For each embodiment it builds dummy inputs generically from the
    checkpoint metadata (state dims from `statistics.json`; camera/language keys from
-   the processor modality configs), runs the original model, and saves to one `.npz`
-   per tag: the raw observations (`raw::` keys), the exact collated inputs
-   (`in::` keys), the seed, and the raw `action_pred`.
-2. **Consumer** — the pytest above, run in the _LeRobot_ venv. It discovers every
-   `.npz`; the model-parity case replays the byte-identical collated inputs through
-   the LeRobot model with the recorded seed and asserts the outputs match, and the
-   preprocessor-parity case replays the raw observations through LeRobot's full
-   preprocessor pipeline and asserts the collated tensors match.
-
-> Artifacts generated by older versions of the dump script contain no `raw::`
-> fields; the preprocessor-parity case then **skips** with a regeneration hint.
-> Re-run the producer to refresh them.
+   the processor modality configs), runs the original model, and saves the exact
+   collated inputs + raw `action_pred` to one `.npz` per tag.
+2. **Consumer** — the pytest above, run in the *LeRobot* venv. It discovers every
+   `.npz`, replays the byte-identical inputs through the LeRobot model with the same
+   seed, and asserts the outputs match.
 
 ### Fairness controls
 
-- **Same pre-processed inputs (model parity)** — the original processor's `input_ids`,
+- **Same pre-processed inputs** — the original processor's `input_ids`,
   `pixel_values`, `image_grid_thw`, `attention_mask`, `state`, `embodiment_id` are
-  fed verbatim to the LeRobot model (no re-tokenization / re-normalization), so the
-  model comparison isolates the model. LeRobot's own tokenization / image packing is
-  covered separately by the preprocessor-parity case, which compares its output
-  against those same collated tensors from identical raw observations.
+  fed verbatim to the LeRobot model (no re-tokenization / re-normalization).
 - **Same precision + attention kernel** — both sides run **fp32 + SDPA**. The
   original defaults to `use_flash_attention=True` (flash_attention_2 + bf16); the
   producer forces SDPA + fp32. (With the defaults the gap is ~3e-2 — pure
   kernel/rounding noise, not an implementation difference.)
-- **Same flow-matching seed** — fixed right before sampling on both sides; the
-  producer records it in each artifact (`--seed`, default 42) and the consumer
-  replays the recorded value.
+- **Same flow-matching seed** — fixed (42) right before sampling on both sides.
 
 ### How to run
 
@@ -119,15 +90,15 @@ CUDA_VISIBLE_DEVICES=0 GROOT_PARITY_DEVICE=cuda \
     uv run pytest tests/policies/groot/test_groot_vs_original.py -v -s
 ```
 
-The `.npz` artifacts are local-only (gitignored, ~6–10 MB each) and are regenerated by
-the producer; they are never committed. The tests **skip** (do not fail) on CI or
+The `.npz` artifacts are local-only (gitignored, ~6–9 MB each) and are regenerated by
+the producer; they are never committed. The test **skips** (does not fail) on CI or
 when the checkpoint / artifacts are absent.
 
 #### Env knobs (all optional)
 
-| Var                                       | Default                          | Purpose                               |
-| ----------------------------------------- | -------------------------------- | ------------------------------------- |
-| `GROOT_N1_7_PARITY_DIR`                   | `tests/policies/groot/artifacts` | directory of per-tag `.npz` artifacts |
-| `GROOT_N1_7_LIBERO_CKPT`                  | auto (HF cache)                  | override checkpoint dir               |
-| `GROOT_PARITY_DEVICE`                     | `cuda` if available              | `cpu` or `cuda`                       |
-| `GROOT_PARITY_ATOL` / `GROOT_PARITY_RTOL` | `1e-3`                           | comparison tolerance                  |
+| Var | Default | Purpose |
+|---|---|---|
+| `GROOT_N1_7_PARITY_DIR` | `tests/policies/groot/artifacts` | directory of per-tag `.npz` artifacts |
+| `GROOT_N1_7_LIBERO_CKPT` | auto (HF cache) | override checkpoint dir |
+| `GROOT_PARITY_DEVICE` | `cuda` if available | `cpu` or `cuda` |
+| `GROOT_PARITY_ATOL` / `GROOT_PARITY_RTOL` | `1e-3` | comparison tolerance |

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/action_head/cross_attention_dit.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 
 
-import logging
 from typing import TYPE_CHECKING
 
 import torch
@@ -43,9 +42,6 @@ else:
     Timesteps = None
 
 
-logger = logging.getLogger(__name__)
-
-
 class TimestepEncoder(nn.Module):
     def __init__(self, embedding_dim, compute_dtype=torch.float32):
         require_package("diffusers", extra="groot")
@@ -269,8 +265,8 @@ class DiT(ModelMixin, ConfigMixin):
         self.norm_out = nn.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6)
         self.proj_out_1 = nn.Linear(self.inner_dim, 2 * self.inner_dim)
         self.proj_out_2 = nn.Linear(self.inner_dim, self.config.output_dim)
-        logger.debug(
-            "Total number of DiT parameters: %d",
+        print(
+            "Total number of DiT parameters: ",
             sum(p.numel() for p in self.parameters() if p.requires_grad),
         )
 
@@ -430,8 +426,8 @@ class SelfAttentionTransformer(ModelMixin, ConfigMixin):
                 for _ in range(self.config.num_layers)
             ]
         )
-        logger.debug(
-            "Total number of SelfAttentionTransformer parameters: %d",
+        print(
+            "Total number of SelfAttentionTransformer parameters: ",
             sum(p.numel() for p in self.parameters() if p.requires_grad),
         )
 

src/lerobot/policies/groot/configuration_groot.py

@@ -14,7 +14,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import logging
+import json
+import os
 from dataclasses import dataclass, field
 from pathlib import Path
 
@@ -22,35 +23,15 @@ 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"
 # Legacy GR00T N1.5 identifier. N1.5 is NOT a supported model_version (it is
 # intentionally absent from _GROOT_MODEL_VERSION_ALIASES so normalize_groot_model_version
 # still rejects it). It is retained only so that infer_groot_model_version can recognise
 # an N1.5 base path/checkpoint and the N1.7 config/loader can reject the mismatch.
 GROOT_N1_5 = "n1.5"
-# Canonical guidance appended to every error raised when an N1.5 checkpoint, config,
-# or processor pipeline is detected. Keep this message in sync with docs/source/groot.mdx.
-GROOT_N1_5_REMOVAL_GUIDANCE = (
-    "GR00T N1.5 support was removed from LeRobot. "
-    "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)."
-)
 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
-# explicit 'none' (resolved to None) so an opt-out survives a draccus save/load round-trip.
-GROOT_ACTION_DECODE_TRANSFORM_AUTO = "auto"
 
 _GROOT_MODEL_VERSION_ALIASES = {
     "n1.7": GROOT_N1_7,
@@ -60,12 +41,7 @@ _GROOT_MODEL_VERSION_ALIASES = {
     "1.7": GROOT_N1_7,
 }
 
-# Legacy N1.5 spellings, kept ONLY so they can be detected and rejected with
-# GROOT_N1_5_REMOVAL_GUIDANCE (see GROOT_N1_5 above). Never map these to a supported version.
-_GROOT_N1_5_VERSION_ALIASES = {"n1.5", "n1_5", "n1d5", "n15", "1.5"}
-
 _GROOT_ACTION_DECODE_TRANSFORM_ALIASES = {
-    GROOT_ACTION_DECODE_TRANSFORM_AUTO: GROOT_ACTION_DECODE_TRANSFORM_AUTO,
     "none": None,
     "": None,
     GROOT_ACTION_DECODE_TRANSFORM_LIBERO: GROOT_ACTION_DECODE_TRANSFORM_LIBERO,
@@ -76,10 +52,9 @@ def normalize_groot_model_version(model_version: str) -> str:
     normalized = _GROOT_MODEL_VERSION_ALIASES.get(model_version.lower())
     if normalized is None:
         supported = GROOT_N1_7
-        message = f"Unsupported GR00T model_version '{model_version}'. Supported versions: {supported}."
-        if model_version.lower() in _GROOT_N1_5_VERSION_ALIASES:
-            message = f"{message} {GROOT_N1_5_REMOVAL_GUIDANCE}"
-        raise ValueError(message)
+        raise ValueError(
+            f"Unsupported GR00T model_version '{model_version}'. Supported versions: {supported}."
+        )
     return normalized
 
 
@@ -127,7 +102,12 @@ 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 +116,11 @@ 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 +139,11 @@ 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 +185,83 @@ 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():
@@ -206,14 +271,21 @@ 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:
     model_version = config.get("model_version")
     if isinstance(model_version, str):
-        if model_version.lower() in _GROOT_N1_5_VERSION_ALIASES:
-            return GROOT_N1_5
         try:
             return normalize_groot_model_version(model_version)
         except ValueError:
@@ -226,14 +298,8 @@ 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
-        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:
         return GROOT_N1_7
-    # The Eagle VLM backbone is specific to pre-N1.7 GR00T checkpoints (N1.7 uses Cosmos/Qwen3-VL).
-    backbone_cfg = config.get("backbone_cfg")
-    if isinstance(backbone_cfg, dict) and "eagle_path" in backbone_cfg:
-        return GROOT_N1_5
     return None
 
 
@@ -244,44 +310,41 @@ class GrootConfig(PreTrainedConfig):
 
     # Basic policy settings
     n_obs_steps: int = 1
-    chunk_size: int = 40
-    n_action_steps: int = 40
+    chunk_size: int = 50
+    n_action_steps: int = 50
 
     # Dimension settings (must match pretrained GR00T model expectations)
     # Maximum state dimension. Shorter states will be zero-padded.
-    max_state_dim: int = 132
+    max_state_dim: int = 64
 
     # Maximum action dimension. Shorter actions will be zero-padded.
-    max_action_dim: int = 132
+    max_action_dim: int = 32
 
-    # GR00T normalizes state/action internally in its processor steps (min/max with
-    # q01/q99 percentiles, per embodiment), and the Qwen3-VL backbone's image processor
-    # handles image normalization. The policy therefore does NOT use LeRobot's
-    # NormalizerProcessorStep/UnnormalizerProcessorStep, so this mapping is intentionally
-    # IDENTITY for every feature and is not consulted by make_groot_pre_post_processors.
+    # Normalization (start with identity, adjust as needed)
     normalization_mapping: dict[str, NormalizationMode] = field(
         default_factory=lambda: {
             "VISUAL": NormalizationMode.IDENTITY,
-            "STATE": NormalizationMode.IDENTITY,
-            "ACTION": NormalizationMode.IDENTITY,
+            "STATE": NormalizationMode.MEAN_STD,
+            "ACTION": NormalizationMode.MEAN_STD,
         }
     )
 
-    # Groot-specific model parameters
+    # Image preprocessing (adjust to match Groot's expected input)
+    image_size: tuple[int, int] = (224, 224)
 
-    # 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
+    action_decode_transform: str | None = None
 
     # Embodiment tag to use for training (e.g. 'new_embodiment', 'gr1')
     embodiment_tag: str = "new_embodiment"
@@ -300,31 +363,20 @@ 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
@@ -332,22 +384,17 @@ 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
+    # Dataset parameters
+    # Video backend to use for training ('decord' or 'torchvision_av')
     video_backend: str = "decord"
+
+    # Whether to balance dataset weights in mixture datasets
     balance_dataset_weights: bool = True
+
+    # Whether to sample trajectories weighted by their length
     balance_trajectory_weights: bool = True
+
+    # Optional dataset paths for delegating training to Isaac-GR00T runner
     dataset_paths: list[str] | None = None
     output_dir: str = "./tmp/gr00t"
     save_steps: int = 1000
@@ -358,12 +405,7 @@ class GrootConfig(PreTrainedConfig):
     resume: bool = False
 
     def __post_init__(self):
-        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
@@ -374,48 +416,26 @@ class GrootConfig(PreTrainedConfig):
         # 'libero_sim' embodiment grasps correctly instead of scoring 0% success.
         # This matches the embodiment-specific handling already done for the
         # action execution horizon (see infer_groot_n1_7_action_execution_horizon).
-        # Only the 'auto' sentinel resolves to the embodiment default; an explicit
-        # 'none' (normalized to None above) keeps the transform disabled.
-        if self.action_decode_transform == GROOT_ACTION_DECODE_TRANSFORM_AUTO:
-            self.action_decode_transform = (
-                GROOT_ACTION_DECODE_TRANSFORM_LIBERO if self.embodiment_tag == "libero_sim" else None
-            )
+        if self.action_decode_transform is None and self.embodiment_tag == "libero_sim":
+            self.action_decode_transform = GROOT_ACTION_DECODE_TRANSFORM_LIBERO
 
-        # GR00T N1.5-era default values (e.g. --policy.chunk_size=50 from old commands or
-        # stale configs) are migrated to the values the N1.7 checkpoints expect, with a
-        # warning. The dataclass defaults are already the N1.7 values, so a plain
-        # GrootConfig() never triggers this.
-        legacy_default_remaps = (
-            ("max_state_dim", 64, 132),
-            ("max_action_dim", 32, 132),
-            ("chunk_size", 50, 40),
-            ("n_action_steps", 50, 40),
-            ("image_size", (224, 224), (256, 256)),
-        )
-        for field_name, legacy_value, n1_7_value in legacy_default_remaps:
-            current_value = getattr(self, field_name)
-            if isinstance(legacy_value, tuple):
-                current_value = tuple(current_value)
-            if current_value == legacy_value:
-                logger.warning(
-                    "GrootConfig.%s=%s matches a legacy GR00T N1.5-era default; remapping it to %s, "
-                    "the value expected by GR00T N1.7 checkpoints. Set a different value explicitly "
-                    "if this is not what you want.",
-                    field_name,
-                    legacy_value,
-                    n1_7_value,
-                )
-                setattr(self, field_name, n1_7_value)
+        if self.max_state_dim == 64:
+            self.max_state_dim = 132
+        if self.max_action_dim == 32:
+            self.max_action_dim = 132
+        if self.chunk_size == 50:
+            self.chunk_size = 40
+        if self.n_action_steps == 50:
+            self.n_action_steps = 40
+        if tuple(self.image_size) == (224, 224):
+            self.image_size = (256, 256)
 
         inferred_version = infer_groot_model_version(self.base_model_path)
-        if inferred_version is not None and inferred_version != GROOT_N1_7:
-            message = (
-                f"GR00T model_version '{GROOT_N1_7}' does not match base_model_path "
+        if inferred_version is not None and inferred_version != self.model_version:
+            raise ValueError(
+                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:
-                message = f"{message} {GROOT_N1_5_REMOVAL_GUIDANCE}"
-            raise ValueError(message)
 
         super().__post_init__()
 
@@ -424,6 +444,9 @@ 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]
@@ -489,9 +512,7 @@ class GrootConfig(PreTrainedConfig):
     @property
     def action_delta_indices(self) -> list[int]:
         """Return indices for delta actions."""
-        model_action_horizon = (
-            infer_groot_n1_7_action_horizon(self.base_model_path, self.embodiment_tag) or 40
-        )
+        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)))
 
     @property

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": 12,
     "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,
@@ -783,14 +819,11 @@ def _cosmos_reason2_qwen3_vl_config() -> PretrainedConfig:
 
 
 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":
-        logger.warning(
-            "Unrecognized GR00T N1.7 backbone model name '%s'; assuming a Qwen3-VL-compatible "
-            "backbone because backbone_model_type='qwen'.",
-            config.model_name,
-        )
+    if (
+        config.backbone_model_type == "qwen"
+        or "nvidia/Cosmos-Reason2" in config.model_name
+        or "Qwen/Qwen3-VL" in config.model_name
+    ):
         return Qwen3Backbone
     raise ValueError(f"Unsupported GR00T N1.7 backbone model: {config.model_name}")
 
@@ -807,7 +840,6 @@ 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
@@ -877,7 +909,7 @@ class GR00TN17(PreTrainedModel):
             "trust_remote_code": True
         }
         load_backbone_weights = kwargs.pop("load_backbone_weights", False)
-        for key in ("cache_dir", "local_files_only", "token"):
+        for key in ("revision", "cache_dir", "local_files_only", "token"):
             if key in kwargs:
                 transformers_loading_kwargs.setdefault(key, kwargs[key])
 
@@ -913,12 +945,6 @@ 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 +957,6 @@ def _register_with_transformers() -> None:
     except TypeError:
         with suppress(ValueError):
             AutoModel.register(GR00TN17Config, GR00TN17)
+
+
+_register_with_transformers()

src/lerobot/policies/groot/modeling_groot.py

@@ -18,21 +18,21 @@
 Groot Policy Wrapper for LeRobot Integration
 
 Minimal integration that delegates to Isaac-GR00T N1.7 components where
-possible without porting their code. Dataset loading and training
-orchestration are handled by LeRobot's standard training stack.
+possible without porting their code.
+
+Notes:
+- Dataset loading and full training orchestration is handled by Isaac-GR00T
+  TrainRunner in their codebase. If you want to invoke that flow end-to-end
+  from LeRobot, see `GrootPolicy.finetune_with_groot_runner` below.
 """
 
 import builtins
-import logging
 import os
 from collections import deque
 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
@@ -42,17 +42,13 @@ from lerobot.utils.import_utils import require_package
 from ..pretrained import PreTrainedPolicy
 from ..utils import get_device_from_parameters
 from .configuration_groot import (
-    GROOT_N1_5,
-    GROOT_N1_5_REMOVAL_GUIDANCE,
     GROOT_N1_7,
     GrootConfig,
     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__)
 
 T = TypeVar("T", bound="GrootPolicy")
 
@@ -77,30 +73,30 @@ 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},
         )
 
+        model.compute_dtype = "bfloat16" if self.config.use_bf16 else model.compute_dtype
+        model.config.compute_dtype = model.compute_dtype
+
+        return model
+
     def reset(self):
         """Reset policy state when environment resets."""
         self._action_queue = deque([], maxlen=self._action_queue_steps)
@@ -143,11 +139,18 @@ 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
-        logger.info(
-            "The Groot policy wraps NVIDIA's GR00T %s model. Loading pretrained model from: %s",
-            requested_version,
-            pretrained_name_or_path,
+        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
+        )
+        print(
+            f"The Groot policy is a wrapper around Nvidia's GR00T {requested_version} model.\n"
+            f"Loading pretrained model from: {pretrained_name_or_path}"
         )
 
         model_id = str(pretrained_name_or_path)
@@ -178,7 +181,7 @@ class GrootPolicy(PreTrainedPolicy):
 
         if is_finetuned_checkpoint:
             # This is a fine-tuned LeRobot checkpoint - use parent class loading
-            logger.info("Detected fine-tuned LeRobot checkpoint, loading with state dict...")
+            print("Detected fine-tuned LeRobot checkpoint, loading with state dict...")
             return super().from_pretrained(
                 pretrained_name_or_path=pretrained_name_or_path,
                 config=config,
@@ -194,11 +197,13 @@ class GrootPolicy(PreTrainedPolicy):
             )
 
         # This is a base GR00T model - load it fresh
-        logger.info("Detected base GR00T model, loading from HuggingFace...")
+        print("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),
             )
 
@@ -221,15 +226,13 @@ 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:
-            message = (
-                f"GR00T model_version '{GROOT_N1_7}' does not match base_model_path "
+        if inferred_version is not None and inferred_version != config.model_version:
+            raise ValueError(
+                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:
-                message = f"{message} {GROOT_N1_5_REMOVAL_GUIDANCE}"
-            raise ValueError(message)
         # Create a fresh policy instance - this will automatically load the GR00T model
         # in __init__ via _create_groot_model()
         policy = cls(config)
@@ -294,7 +297,9 @@ class GrootPolicy(PreTrainedPolicy):
         allowed_base.add("action_mask")
 
         return {
-            k: v for k, v in batch.items() if k in allowed_base and not (k.startswith("next.") or k == "info")
+            k: v
+            for k, v in batch.items()
+            if k in allowed_base and not (k.startswith("next.") or k == "info")
         }
 
     def _prepare_n1_7_rtc_inputs(
@@ -315,7 +320,9 @@ class GrootPolicy(PreTrainedPolicy):
         if prev_actions.ndim == 2:
             prev_actions = prev_actions.unsqueeze(0)
         elif prev_actions.ndim != 3:
-            raise ValueError("prev_chunk_left_over must have shape (T, A) or (B, T, A) for GR00T N1.7 RTC.")
+            raise ValueError(
+                "prev_chunk_left_over must have shape (T, A) or (B, T, A) for GR00T N1.7 RTC."
+            )
 
         state = inputs.get("state")
         if state is None:
@@ -324,7 +331,9 @@ class GrootPolicy(PreTrainedPolicy):
         if prev_actions.shape[0] == 1 and batch_size > 1:
             prev_actions = prev_actions.expand(batch_size, -1, -1).clone()
         elif prev_actions.shape[0] != batch_size:
-            raise ValueError("prev_chunk_left_over batch size must match the current GR00T N1.7 batch size.")
+            raise ValueError(
+                "prev_chunk_left_over batch size must match the current GR00T N1.7 batch size."
+            )
 
         # The generic LeRobot RTC engine pads short leftovers with exact zero
         # rows for fixed-shape policy calls. Native GR00T N1.7 RTC treats every
@@ -337,9 +346,7 @@ class GrootPolicy(PreTrainedPolicy):
         else:
             return inputs, None
 
-        model_action_horizon = int(
-            getattr(self._groot_model.config, "action_horizon", self.config.chunk_size)
-        )
+        model_action_horizon = int(getattr(self._groot_model.config, "action_horizon", self.config.chunk_size))
         max_action_dim = int(getattr(self._groot_model.config, "max_action_dim", self.config.max_action_dim))
         if prev_actions.shape[1] > model_action_horizon:
             prev_actions = prev_actions[:, -model_action_horizon:, :]
@@ -402,11 +409,6 @@ class GrootPolicy(PreTrainedPolicy):
 
         # Isaac-GR00T returns a BatchFeature; loss key is typically 'loss'
         loss = outputs.get("loss")
-        if loss is None:
-            raise RuntimeError(
-                "GR00T model.forward did not return a 'loss'. Training batches must include "
-                "'action' and 'action_mask'; check the preprocessor output."
-            )
 
         loss_dict = {"loss": loss.item()}
 
@@ -427,11 +429,13 @@ 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 +466,38 @@ 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:
+        """Handle Flash Attention compatibility issues by setting environment variables.
+
+        This addresses the common 'undefined symbol' error that occurs when Flash Attention
+        is compiled against a different PyTorch version than what's currently installed.
+        """
+
+        # Set environment variables to handle Flash Attention compatibility
+        # These help with symbol resolution issues
+        os.environ.setdefault("FLASH_ATTENTION_FORCE_BUILD", "0")
+        os.environ.setdefault("FLASH_ATTENTION_SKIP_CUDA_BUILD", "0")
+
+        # Try to import flash_attn and handle failures gracefully
+        try:
+            import flash_attn
+
+            print(f"[GROOT] Flash Attention version: {flash_attn.__version__}")
+        except ImportError as e:
+            print(f"[GROOT] Flash Attention not available: {e}")
+            print("[GROOT] Will use fallback attention mechanism")
+        except Exception as e:
+            if "undefined symbol" in str(e):
+                print(f"[GROOT] Flash Attention compatibility issue detected: {e}")
+                print("[GROOT] This is likely due to PyTorch/Flash Attention version mismatch")
+                print("[GROOT] Consider reinstalling Flash Attention with compatible version:")
+                print("  pip uninstall flash-attn")
+                print("  pip install --no-build-isolation flash-attn==2.6.3")
+                print("[GROOT] Continuing with fallback attention mechanism")
+            else:
+                print(f"[GROOT] Flash Attention error: {e}")
+                print("[GROOT] Continuing with fallback attention mechanism")

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_n1_7.py

@@ -41,7 +41,7 @@ from lerobot.policies.groot.processor_groot import (
     GrootN17ActionDecodeStep,
     GrootN17PackInputsStep,
     GrootN17VLMEncodeStep,
-    _transform_n1_7_image_for_vlm_albumentations,
+    _transform_n1_7_image_for_vlm,
     make_groot_pre_post_processors,
 )
 from lerobot.processor import (
@@ -1529,12 +1529,13 @@ def test_groot_n1_7_vlm_image_transform_matches_albumentations_eval_path():
 
     image_np = (np.arange(360 * 360 * 3, dtype=np.uint32) % 251).astype(np.uint8).reshape(360, 360, 3)
 
-    transformed = _transform_n1_7_image_for_vlm_albumentations(
+    transformed = _transform_n1_7_image_for_vlm(
         Image.fromarray(image_np),
         image_crop_size=[230, 230],
         image_target_size=[256, 256],
         shortest_image_edge=256,
         crop_fraction=0.95,
+        use_albumentations=True,
     )
 
     expected = cv2.resize(image_np, (256, 256), interpolation=cv2.INTER_AREA)

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'" },
     { name = "lerobot", extras = ["grpcio-dep"], marker = "extra == 'dev'" },
     { name = "lerobot", extras = ["grpcio-dep"], marker = "extra == 'hilserl'" },
@@ -3214,6 +3224,7 @@ requires-dist = [
     { name = "motorbridge", marker = "extra == 'motorbridge-dep'", specifier = ">=0.3.2,<0.4.0" },
     { name = "motorbridge-smart-servo", marker = "extra == 'motorbridge-smart-servo-dep'", specifier = ">=0.0.4,<0.1.0" },
     { name = "mypy", marker = "extra == 'dev'", specifier = ">=1.19.1" },
+    { name = "ninja", marker = "extra == 'groot'", specifier = ">=1.11.1,<2.0.0" },
     { name = "num2words", marker = "extra == 'smolvla'", specifier = ">=0.5.14,<0.6.0" },
     { name = "numpy", specifier = ">=2.0.0,<2.3.0" },
     { name = "onnx", marker = "extra == 'unitree-g1'", specifier = ">=1.16.0,<2.0.0" },
@@ -4001,6 +4012,32 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/9e/c9/b2622292ea83fbb4ec318f5b9ab867d0a28ab43c5717bb85b0a5f6b3b0a4/networkx-3.6.1-py3-none-any.whl", hash = "sha256:d47fbf302e7d9cbbb9e2555a0d267983d2aa476bac30e90dfbe5669bd57f3762", size = 2068504, upload-time = "2025-12-08T17:02:38.159Z" },
 ]
 
+[[package]]
+name = "ninja"
+version = "1.13.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/43/73/79a0b22fc731989c708068427579e840a6cf4e937fe7ae5c5d0b7356ac22/ninja-1.13.0.tar.gz", hash = "sha256:4a40ce995ded54d9dc24f8ea37ff3bf62ad192b547f6c7126e7e25045e76f978", size = 242558, upload-time = "2025-08-11T15:10:19.421Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/3c/74/d02409ed2aa865e051b7edda22ad416a39d81a84980f544f8de717cab133/ninja-1.13.0-py3-none-macosx_10_9_universal2.whl", hash = "sha256:fa2a8bfc62e31b08f83127d1613d10821775a0eb334197154c4d6067b7068ff1", size = 310125, upload-time = "2025-08-11T15:09:50.971Z" },
+    { url = "https://files.pythonhosted.org/packages/8e/de/6e1cd6b84b412ac1ef327b76f0641aeb5dcc01e9d3f9eee0286d0c34fd93/ninja-1.13.0-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:3d00c692fb717fd511abeb44b8c5d00340c36938c12d6538ba989fe764e79630", size = 177467, upload-time = "2025-08-11T15:09:52.767Z" },
+    { url = "https://files.pythonhosted.org/packages/c8/83/49320fb6e58ae3c079381e333575fdbcf1cca3506ee160a2dcce775046fa/ninja-1.13.0-py3-none-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:be7f478ff9f96a128b599a964fc60a6a87b9fa332ee1bd44fa243ac88d50291c", size = 187834, upload-time = "2025-08-11T15:09:54.115Z" },
+    { url = "https://files.pythonhosted.org/packages/56/c7/ba22748fb59f7f896b609cd3e568d28a0a367a6d953c24c461fe04fc4433/ninja-1.13.0-py3-none-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:60056592cf495e9a6a4bea3cd178903056ecb0943e4de45a2ea825edb6dc8d3e", size = 202736, upload-time = "2025-08-11T15:09:55.745Z" },
+    { url = "https://files.pythonhosted.org/packages/79/22/d1de07632b78ac8e6b785f41fa9aad7a978ec8c0a1bf15772def36d77aac/ninja-1.13.0-py3-none-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:1c97223cdda0417f414bf864cfb73b72d8777e57ebb279c5f6de368de0062988", size = 179034, upload-time = "2025-08-11T15:09:57.394Z" },
+    { url = "https://files.pythonhosted.org/packages/ed/de/0e6edf44d6a04dabd0318a519125ed0415ce437ad5a1ec9b9be03d9048cf/ninja-1.13.0-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:fb46acf6b93b8dd0322adc3a4945452a4e774b75b91293bafcc7b7f8e6517dfa", size = 180716, upload-time = "2025-08-11T15:09:58.696Z" },
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