fix test relative expr

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

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

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

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

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

docs/source/groot.mdx

@@ -4,6 +4,9 @@ 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, configs, and `--policy.model_version=n1.5` are rejected with a clear error. To keep using an N1.5 checkpoint, pin the last release that supports it: `pip install 'lerobot==0.5.1'`. To use the current release, migrate to GR00T N1.7 (`model_version='n1.7'`, base model [`nvidia/GR00T-N1.7-3B`](https://huggingface.co/nvidia/GR00T-N1.7-3B)).
+
 ## Model Overview
 
 GR00T N1.7 uses a Cosmos-Reason2/Qwen3-VL backbone and provides checkpoints for SimplerEnv, DROID, and LIBERO.
@@ -133,7 +136,7 @@ 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
-huggingface-cli download nvidia/GR00T-N1.7-LIBERO \
+hf download nvidia/GR00T-N1.7-LIBERO \
   --include "libero_spatial/*" \
   --local-dir ./GR00T-N1.7-LIBERO
 

docs/source/policy_groot_README.md

@@ -1,6 +1,13 @@
 ## Research Paper
 
-Paper: https://research.nvidia.com/labs/gear/gr00t-n1_5/
+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.
 
 ## Repository
 
@@ -31,12 +38,22 @@ Hugging Face Models:
 
 ## Original-vs-LeRobot parity test
 
-`tests/policies/groot/test_groot_vs_original.py` verifies that this LeRobot
+`tests/policies/groot/test_groot_vs_original.py` verifies this LeRobot
 reimplementation of GR00T N1.7 (Qwen3-VL backbone + flow-matching action head)
-produces the **same raw model output** (`get_action(...)["action_pred"]`, the
+against NVIDIA's original `gr00t` package with two comparisons, each parametrized
-normalized flow-matching prediction) as NVIDIA's original `gr00t` package, given
+over every embodiment tag present in the checkpoint:
-byte-identical pre-processed inputs and the same flow-matching seed. It is
+
-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.
 
 ### Why two environments
 
@@ -48,25 +65,37 @@ 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 the exact
+   the processor modality configs), runs the original model, and saves to one `.npz`
-   collated inputs + raw `action_pred` to one `.npz` per tag.
+   per tag: the raw observations (`raw::` keys), the exact collated inputs
-2. **Consumer** — the pytest above, run in the *LeRobot* venv. It discovers every
+   (`in::` keys), the seed, and the raw `action_pred`.
-   `.npz`, replays the byte-identical inputs through the LeRobot model with the same
+2. **Consumer** — the pytest above, run in the _LeRobot_ venv. It discovers every
-   seed, and asserts the outputs match.
+   `.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.
 
 ### Fairness controls
 
-- **Same pre-processed inputs** — the original processor's `input_ids`,
+- **Same pre-processed inputs (model parity)** — 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).
+  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.
 - **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 (42) right before sampling on both sides.
+- **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.
 
 ### How to run
 
@@ -90,15 +119,15 @@ 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, ~6–9 MB each) and are regenerated by
+The `.npz` artifacts are local-only (gitignored, ~6–10 MB each) and are regenerated by
-the producer; they are never committed. The test **skips** (does not fail) on CI or
+the producer; they are never committed. The tests **skip** (do not fail) on CI or
 when the checkpoint / artifacts are absent.
 
 #### Env knobs (all optional)
 
-| Var | Default | Purpose |
+| Var                                       | Default                          | Purpose                               |
-|---|---|---|
+| ----------------------------------------- | -------------------------------- | ------------------------------------- |
-| `GROOT_N1_7_PARITY_DIR` | `tests/policies/groot/artifacts` | directory of per-tag `.npz` artifacts |
+| `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_N1_7_LIBERO_CKPT`                  | auto (HF cache)                  | override checkpoint dir               |
-| `GROOT_PARITY_DEVICE` | `cuda` if available | `cpu` or `cuda` |
+| `GROOT_PARITY_DEVICE`                     | `cuda` if available              | `cpu` or `cuda`                       |
-| `GROOT_PARITY_ATOL` / `GROOT_PARITY_RTOL` | `1e-3` | comparison tolerance |
+| `GROOT_PARITY_ATOL` / `GROOT_PARITY_RTOL` | `1e-3`                           | comparison tolerance                  |

src/lerobot/policies/groot/action_head/cross_attention_dit.py

@@ -14,6 +14,7 @@
 # limitations under the License.
 
 
+import logging
 from typing import TYPE_CHECKING
 
 import torch
@@ -42,6 +43,9 @@ 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")
@@ -265,8 +269,8 @@ 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)
-        print(
+        logger.debug(
-            "Total number of DiT parameters: ",
+            "Total number of DiT parameters: %d",
             sum(p.numel() for p in self.parameters() if p.requires_grad),
         )
 
@@ -426,8 +430,8 @@ class SelfAttentionTransformer(ModelMixin, ConfigMixin):
                 for _ in range(self.config.num_layers)
             ]
         )
-        print(
+        logger.debug(
-            "Total number of SelfAttentionTransformer parameters: ",
+            "Total number of SelfAttentionTransformer parameters: %d",
             sum(p.numel() for p in self.parameters() if p.requires_grad),
         )
 

src/lerobot/policies/groot/configuration_groot.py

@@ -15,6 +15,7 @@
 # limitations under the License.
 
 import json
+import logging
 import os
 from dataclasses import dataclass, field
 from pathlib import Path
@@ -23,15 +24,37 @@ from lerobot.configs import FeatureType, NormalizationMode, PolicyFeature, PreTr
 from lerobot.optim import AdamWConfig, CosineDecayWithWarmupSchedulerConfig
 from lerobot.utils.constants import ACTION, OBS_STATE
 
+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"
+# Image preprocessing geometry the GR00T N1.7 backbone was trained on. The processor
+# falls back to these when a checkpoint ships no image sizing in its processor_config
+# (e.g. fine-tuning the raw nvidia/GR00T-N1.7-3B base with a new embodiment), so frames
+# are resized to the expected resolution instead of being patchified at full camera
+# resolution (which both slows training and is a train/checkpoint distribution mismatch).
+# 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,
@@ -41,7 +64,12 @@ _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,
@@ -52,9 +80,10 @@ 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
-        raise ValueError(
+        message = f"Unsupported GR00T model_version '{model_version}'. Supported versions: {supported}."
-            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)
     return normalized
 
 
@@ -286,6 +315,8 @@ def _infer_groot_model_version_from_local_config(model_path: str) -> str | None:
 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:
@@ -298,8 +329,14 @@ 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
 
 
@@ -310,29 +347,30 @@ class GrootConfig(PreTrainedConfig):
 
     # Basic policy settings
     n_obs_steps: int = 1
-    chunk_size: int = 50
+    chunk_size: int = 40
-    n_action_steps: int = 50
+    n_action_steps: int = 40
 
     # Dimension settings (must match pretrained GR00T model expectations)
     # Maximum state dimension. Shorter states will be zero-padded.
-    max_state_dim: int = 64
+    max_state_dim: int = 132
 
     # Maximum action dimension. Shorter actions will be zero-padded.
-    max_action_dim: int = 32
+    max_action_dim: int = 132
 
-    # Normalization (start with identity, adjust as needed)
+    # 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_mapping: dict[str, NormalizationMode] = field(
         default_factory=lambda: {
             "VISUAL": NormalizationMode.IDENTITY,
-            "STATE": NormalizationMode.MEAN_STD,
+            "STATE": NormalizationMode.IDENTITY,
-            "ACTION": NormalizationMode.MEAN_STD,
+            "ACTION": NormalizationMode.IDENTITY,
         }
     )
 
-    # Image preprocessing (adjust to match Groot's expected input)
+    # Groot-specific model parameters
-    image_size: tuple[int, int] = (224, 224)
-
-    # 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
@@ -344,11 +382,47 @@ class GrootConfig(PreTrainedConfig):
     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().
-    action_decode_transform: str | None = None
+    # '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
 
     # Embodiment tag to use for training (e.g. 'new_embodiment', 'gr1')
     embodiment_tag: str = "new_embodiment"
 
+    # Inference-only override for the number of flow-matching denoising steps used to decode an
+    # action chunk. None = use the model checkpoint default (currently 4). Higher values trade
+    # inference speed for action quality; applied at base-model load via _create_groot_model.
+    num_inference_timesteps: int | None = None
+
+    # If set, caps the number of open-loop actions executed before replanning (inference cadence).
+    # Overrides the value inferred from the checkpoint/embodiment in _resolve_action_queue_steps.
+    execution_horizon: int | None = None
+
+    # Opt-in. Copy a pretrained embodiment category slot's action-head weights into the target
+    # embodiment slot at base-model build (in _create_groot_model), to warm-start a cold
+    # 'new_embodiment' slot. Accepts an embodiment name (e.g.
+    # 'oxe_droid_relative_eef_relative_joint') or an int embodiment id. Runs on every fresh
+    # base-model build (so it applies during lerobot-train, which uses __init__ not
+    # from_pretrained); on a fine-tuned checkpoint reload it is harmlessly overwritten.
+    warm_start_embodiment_slot: int | str | None = None
+
+    # Opt-in relative-action support for the 'new_embodiment' slot (sync-safe, GR00T-native).
+    # When True, GR00T converts absolute->relative inside its own pack step (training) and
+    # reconstructs absolute inside its own flat decode step (inference), using a cached
+    # reference state. The dataset stays absolute; compute relative ACTION stats with
+    # `lerobot-edit-dataset --operation.relative_action true --operation.relative_exclude_joints
+    # "['gripper']"` (this only rewrites stats, not actions).
+    use_relative_actions: bool = False
+
+    # Joint names kept absolute (not converted to relative) when use_relative_actions is True.
+    # Case-insensitive token match against action_feature_names.
+    relative_exclude_joints: list[str] = field(default_factory=lambda: ["gripper"])
+
+    # Action dimension names from dataset metadata; auto-populated by the factory from dataset
+    # meta (see factory.py:528). Used to build the relative-action mask so the gripper can be
+    # identified and kept absolute. When None, the gripper cannot be identified.
+    action_feature_names: list[str] | None = None
+
     # Fine-tuning control arguments
 
     # Whether to fine-tune the llm backbone
@@ -384,17 +458,16 @@ class GrootConfig(PreTrainedConfig):
     warmup_ratio: float = 0.05
     use_bf16: bool = True
 
-    # Dataset parameters
+    # TODO(Steven): Remove these deprecated fields in a future release.
-    # Video backend to use for training ('decord' or 'torchvision_av')
+    # Deprecated Isaac-GR00T runner/N1.5 fields below — unused by the LeRobot N1.7 implementation
+    # (nothing in src/lerobot reads them). They are kept only so config.json files saved by
+    # earlier lerobot releases still parse: draccus rejects unknown fields, so removing them
+    # would break every previously saved groot checkpoint at config-load time.
+    image_size: tuple[int, int] = (256, 256)  # image sizing is handled by the backbone's image processor.
+    tokenizer_assets_repo: str | None = None
     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
@@ -405,6 +478,12 @@ 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:
@@ -416,26 +495,48 @@ 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).
-        if self.action_decode_transform is None and self.embodiment_tag == "libero_sim":
+        # Only the 'auto' sentinel resolves to the embodiment default; an explicit
-            self.action_decode_transform = GROOT_ACTION_DECODE_TRANSFORM_LIBERO
+        # '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.max_state_dim == 64:
+        # GR00T N1.5-era default values (e.g. --policy.chunk_size=50 from old commands or
-            self.max_state_dim = 132
+        # stale configs) are migrated to the values the N1.7 checkpoints expect, with a
-        if self.max_action_dim == 32:
+        # warning. The dataclass defaults are already the N1.7 values, so a plain
-            self.max_action_dim = 132
+        # GrootConfig() never triggers this.
-        if self.chunk_size == 50:
+        legacy_default_remaps = (
-            self.chunk_size = 40
+            ("max_state_dim", 64, 132),
-        if self.n_action_steps == 50:
+            ("max_action_dim", 32, 132),
-            self.n_action_steps = 40
+            ("chunk_size", 50, 40),
-        if tuple(self.image_size) == (224, 224):
+            ("n_action_steps", 50, 40),
-            self.image_size = (256, 256)
+            ("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)
 
         inferred_version = infer_groot_model_version(self.base_model_path)
         if inferred_version is not None and inferred_version != self.model_version:
-            raise ValueError(
+            message = (
                 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__()
 
@@ -512,7 +613,9 @@ 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

@@ -32,6 +32,7 @@ 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
@@ -71,13 +72,13 @@ GR00T_N1_7_DEFAULTS: dict[str, Any] = {
     "backbone_embedding_dim": 2048,
     "tune_llm": False,
     "tune_visual": False,
-    "select_layer": 12,
+    "select_layer": 16,
     "reproject_vision": False,
     "use_flash_attention": True,
     "load_bf16": False,
     "backbone_trainable_params_fp32": True,
-    "image_crop_size": (230, 230),
+    "image_crop_size": N1_7_DEFAULT_IMAGE_CROP_SIZE,
-    "image_target_size": (256, 256),
+    "image_target_size": N1_7_DEFAULT_IMAGE_TARGET_SIZE,
     "shortest_image_edge": None,
     "crop_fraction": None,
     "random_rotation_angle": None,
@@ -819,11 +820,14 @@ def _cosmos_reason2_qwen3_vl_config() -> PretrainedConfig:
 
 
 def get_backbone_cls(config: GR00TN17Config):
-    if (
+    if "nvidia/Cosmos-Reason2" in config.model_name or "Qwen/Qwen3-VL" in config.model_name:
-        config.backbone_model_type == "qwen"
+        return Qwen3Backbone
-        or "nvidia/Cosmos-Reason2" in config.model_name
+    if config.backbone_model_type == "qwen":
-        or "Qwen/Qwen3-VL" in config.model_name
+        logger.warning(
-    ):
+            "Unrecognized GR00T N1.7 backbone model name '%s'; assuming a Qwen3-VL-compatible "
+            "backbone because backbone_model_type='qwen'.",
+            config.model_name,
+        )
         return Qwen3Backbone
     raise ValueError(f"Unsupported GR00T N1.7 backbone model: {config.model_name}")
 
@@ -909,7 +913,7 @@ class GR00TN17(PreTrainedModel):
             "trust_remote_code": True
         }
         load_backbone_weights = kwargs.pop("load_backbone_weights", False)
-        for key in ("revision", "cache_dir", "local_files_only", "token"):
+        for key in ("cache_dir", "local_files_only", "token"):
             if key in kwargs:
                 transformers_loading_kwargs.setdefault(key, kwargs[key])
 

src/lerobot/policies/groot/modeling_groot.py

@@ -18,15 +18,12 @@
 Groot Policy Wrapper for LeRobot Integration
 
 Minimal integration that delegates to Isaac-GR00T N1.7 components where
-possible without porting their code.
+possible without porting their code. Dataset loading and training
-
+orchestration are handled by LeRobot's standard training stack.
-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
@@ -42,6 +39,8 @@ 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,
@@ -50,9 +49,103 @@ from .configuration_groot import (
     normalize_groot_model_version,
 )
 
+logger = logging.getLogger(__name__)
+
 T = TypeVar("T", bound="GrootPolicy")
 
 
+def _resolve_embodiment_id(value: int | str) -> int:
+    """Resolve an embodiment id from an int or an N1.7 embodiment name.
+
+    Names are looked up in N1_7_EMBODIMENT_MAPPING (e.g. 'new_embodiment' -> 10).
+    Raises ValueError listing the known keys if the name is unknown.
+    """
+    from .processor_groot import N1_7_EMBODIMENT_MAPPING
+
+    if isinstance(value, bool):  # bool is a subclass of int; reject it explicitly.
+        raise ValueError(f"Embodiment id must be an int or embodiment name, got bool {value!r}.")
+    if isinstance(value, int):
+        return value
+    if value in N1_7_EMBODIMENT_MAPPING:
+        return N1_7_EMBODIMENT_MAPPING[value]
+    raise ValueError(
+        f"Unknown GR00T N1.7 embodiment name '{value}'. Known names: "
+        f"{sorted(N1_7_EMBODIMENT_MAPPING.keys())}."
+    )
+
+
+def _warm_start_embodiment_slot(model, source_id: int, target_id: int) -> None:
+    """Copy category-specific action-head weights from one embodiment slot to another.
+
+    Used at base-model load (training only) to warm-start a cold target embodiment slot
+    (e.g. 'new_embodiment') from a pretrained slot. Copies the per-category ``W``/``b``
+    parameters across every CategorySpecificLinear in the action head's state encoder,
+    action encoder, and action decoder. No-ops (with a logged warning) if the ids are out
+    of range or identical.
+    """
+    if source_id == target_id:
+        logger.warning(
+            "GR00T warm_start_embodiment_slot: source and target embodiment id are both %d; "
+            "skipping (nothing to copy).",
+            source_id,
+        )
+        return
+
+    action_head = getattr(model, "action_head", None)
+    if action_head is None:
+        logger.warning("GR00T warm_start_embodiment_slot: model has no action_head; skipping.")
+        return
+
+    # Each entry is (submodule, [CategorySpecificLinear attribute names]).
+    linear_groups = [
+        (getattr(action_head, "state_encoder", None), ["layer1", "layer2"]),
+        (getattr(action_head, "action_encoder", None), ["W1", "W2", "W3"]),
+        (getattr(action_head, "action_decoder", None), ["layer1", "layer2"]),
+    ]
+
+    copied: list[str] = []
+    with torch.no_grad():
+        for submodule, attr_names in linear_groups:
+            if submodule is None:
+                continue
+            submodule_name = type(submodule).__name__
+            for attr_name in attr_names:
+                lin = getattr(submodule, attr_name, None)
+                if lin is None or not hasattr(lin, "W") or not hasattr(lin, "b"):
+                    continue
+                num_categories = lin.W.shape[0]
+                if not (0 <= source_id < num_categories and 0 <= target_id < num_categories):
+                    logger.warning(
+                        "GR00T warm_start_embodiment_slot: source_id=%d/target_id=%d out of range "
+                        "for %s.%s (num_categories=%d); skipping this layer.",
+                        source_id,
+                        target_id,
+                        submodule_name,
+                        attr_name,
+                        num_categories,
+                    )
+                    continue
+                lin.W.data[target_id] = lin.W.data[source_id].clone()
+                lin.b.data[target_id] = lin.b.data[source_id].clone()
+                copied.append(f"{submodule_name}.{attr_name}")
+
+    if copied:
+        logger.info(
+            "GR00T warm_start_embodiment_slot: copied action-head weights from embodiment slot %d "
+            "to slot %d for: %s.",
+            source_id,
+            target_id,
+            ", ".join(copied),
+        )
+    else:
+        logger.warning(
+            "GR00T warm_start_embodiment_slot: no action-head weights were copied "
+            "(source_id=%d, target_id=%d).",
+            source_id,
+            target_id,
+        )
+
+
 class GrootPolicy(PreTrainedPolicy):
     """Wrapper around external Groot model for LeRobot integration."""
 
@@ -92,8 +185,24 @@ class GrootPolicy(PreTrainedPolicy):
             transformers_loading_kwargs={"trust_remote_code": True},
         )
 
-        model.compute_dtype = "bfloat16" if self.config.use_bf16 else model.compute_dtype
+        # Inference-only override for the number of flow-matching denoising steps. The action
-        model.config.compute_dtype = model.compute_dtype
+        # head reads self.num_inference_timesteps in get_action_with_features; dt (1/n) and the
+        # t schedule adapt automatically.
+        if self.config.num_inference_timesteps is not None:
+            n = int(self.config.num_inference_timesteps)
+            model.config.num_inference_timesteps = n
+            model.action_head.num_inference_timesteps = n
+
+        # Opt-in: warm-start a cold embodiment slot (e.g. 'new_embodiment') from a pretrained
+        # slot's action-head weights. Done here (not in from_pretrained) so it applies on every
+        # fresh base-model build -- training via make_policy instantiates GrootPolicy(config)
+        # directly (factory uses __init__ when cfg.pretrained_path is unset), it does NOT go
+        # through from_pretrained. On a fine-tuned checkpoint reload this also runs but is
+        # immediately overwritten by the loaded state_dict, so it is a harmless no-op there.
+        if self.config.warm_start_embodiment_slot is not None:
+            source_id = _resolve_embodiment_id(self.config.warm_start_embodiment_slot)
+            target_id = _resolve_embodiment_id(self.config.embodiment_tag)
+            _warm_start_embodiment_slot(model, source_id, target_id)
 
         return model
 
@@ -148,9 +257,10 @@ class GrootPolicy(PreTrainedPolicy):
             if config is not None
             else infer_groot_model_version(str(pretrained_name_or_path)) or GROOT_N1_7
         )
-        print(
+        logger.info(
-            f"The Groot policy is a wrapper around Nvidia's GR00T {requested_version} model.\n"
+            "The Groot policy wraps NVIDIA's GR00T %s model. Loading pretrained model from: %s",
-            f"Loading pretrained model from: {pretrained_name_or_path}"
+            requested_version,
+            pretrained_name_or_path,
         )
 
         model_id = str(pretrained_name_or_path)
@@ -181,7 +291,7 @@ class GrootPolicy(PreTrainedPolicy):
 
         if is_finetuned_checkpoint:
             # This is a fine-tuned LeRobot checkpoint - use parent class loading
-            print("Detected fine-tuned LeRobot checkpoint, loading with state dict...")
+            logger.info("Detected fine-tuned LeRobot checkpoint, loading with state dict...")
             return super().from_pretrained(
                 pretrained_name_or_path=pretrained_name_or_path,
                 config=config,
@@ -197,7 +307,7 @@ class GrootPolicy(PreTrainedPolicy):
             )
 
         # This is a base GR00T model - load it fresh
-        print("Detected base GR00T model, loading from HuggingFace...")
+        logger.info("Detected base GR00T model, loading from HuggingFace...")
 
         if config is None:
             model_version = infer_groot_model_version(str(pretrained_name_or_path)) or GROOT_N1_7
@@ -229,10 +339,13 @@ class GrootPolicy(PreTrainedPolicy):
         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 != config.model_version:
-            raise ValueError(
+            message = (
                 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)
@@ -258,7 +371,11 @@ class GrootPolicy(PreTrainedPolicy):
             horizons.append(checkpoint_action_horizon)
         if execution_horizon is not None:
             horizons.append(execution_horizon)
-        return min(horizons)
+        # An explicit config override caps the open-loop horizon (inference cadence), overriding
+        # the value inferred from the checkpoint/embodiment.
+        if self.config.execution_horizon is not None:
+            horizons.append(max(1, int(self.config.execution_horizon)))
+        return max(1, min(horizons))
 
     def _resolve_prediction_horizon(self, actions: Tensor) -> int:
         """Return the policy-facing action horizon for a native GR00T prediction."""
@@ -297,9 +414,7 @@ class GrootPolicy(PreTrainedPolicy):
         allowed_base.add("action_mask")
 
         return {
-            k: v
+            k: v for k, v in batch.items() if k in allowed_base and not (k.startswith("next.") or k == "info")
-            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(
@@ -320,9 +435,7 @@ class GrootPolicy(PreTrainedPolicy):
         if prev_actions.ndim == 2:
             prev_actions = prev_actions.unsqueeze(0)
         elif prev_actions.ndim != 3:
-            raise ValueError(
+            raise ValueError("prev_chunk_left_over must have shape (T, A) or (B, T, A) for GR00T N1.7 RTC.")
-                "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:
@@ -331,9 +444,7 @@ 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(
+            raise ValueError("prev_chunk_left_over batch size must match the current GR00T N1.7 batch size.")
-                "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
@@ -346,7 +457,9 @@ 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:, :]
@@ -409,6 +522,11 @@ 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()}
 
@@ -425,6 +543,16 @@ class GrootPolicy(PreTrainedPolicy):
         """
         self.eval()
 
+        # Freeze the relative-action reference at the exact chunk-prediction event so every popped
+        # delta of this chunk is reconstructed (in the postprocessor) against this S_T, not the
+        # per-tick latest state. Driven by the predict event, so it is correct under any runtime
+        # n_action_steps/execution_horizon. No-op for non-relative checkpoints (holder absent/unused).
+        from .processor_groot import _GROOT_REF_HOLDER_KEY
+
+        holder = batch.get(_GROOT_REF_HOLDER_KEY)
+        if holder is not None:
+            holder.freeze()
+
         # Preprocessing is handled by the processor pipeline, so we just filter the batch.
         # 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.
@@ -471,33 +599,21 @@ class GrootPolicy(PreTrainedPolicy):
     # Internal helpers
     # -------------------------
     def _handle_flash_attention_compatibility(self) -> None:
-        """Handle Flash Attention compatibility issues by setting environment variables.
+        """Log Flash Attention availability (diagnostic only).
 
-        This addresses the common 'undefined symbol' error that occurs when Flash Attention
+        The GR00T N1.7 backbone automatically falls back to SDPA when ``flash_attn`` is
-        is compiled against a different PyTorch version than what's currently installed.
+        unavailable (see ``Qwen3Backbone``), so this probe only emits a hint; it does not
+        change behaviour or mutate global state.
         """
-
-        # 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__}")
+            logger.debug("Flash Attention %s is available.", flash_attn.__version__)
-        except ImportError as e:
+        except ImportError:
-            print(f"[GROOT] Flash Attention not available: {e}")
+            logger.debug("Flash Attention is not installed; the GR00T backbone will use SDPA.")
-            print("[GROOT] Will use fallback attention mechanism")
+        except Exception as e:  # noqa: BLE001
-        except Exception as e:
+            logger.warning(
-            if "undefined symbol" in str(e):
+                "Flash Attention failed to import (%s); the GR00T backbone will use SDPA. If this is "
-                print(f"[GROOT] Flash Attention compatibility issue detected: {e}")
+                "an 'undefined symbol' error, reinstall a flash-attn build matching your torch version.",
-                print("[GROOT] This is likely due to PyTorch/Flash Attention version mismatch")
+                e,
-                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")

tests/policies/groot/test_groot_lerobot.py

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