minor fixes

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

@@ -352,12 +352,16 @@ class GrootConfig(PreTrainedConfig):
     # Maximum action dimension. Shorter actions will be zero-padded.
     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,
         }
     )
 
@@ -578,11 +582,22 @@ class GrootConfig(PreTrainedConfig):
 
     @property
     def action_delta_indices(self) -> list[int]:
-        """Return indices for delta actions."""
+        """Return indices for delta actions.
+
+        The model action horizon is read from the checkpoint's processor_config.json
+        when available; the result is cached (keyed on the inputs that determine it) so
+        repeated access during dataset/training setup does not re-read from disk.
+        """
+        cache_key = (self.base_model_path, self.embodiment_tag, self.chunk_size)
+        cached = getattr(self, "_action_delta_indices_cache", None)
+        if cached is not None and cached[0] == cache_key:
+            return cached[1]
         model_action_horizon = (
             infer_groot_n1_7_action_horizon(self.base_model_path, self.embodiment_tag) or 40
         )
-        return list(range(min(self.chunk_size, model_action_horizon)))
+        indices = list(range(min(self.chunk_size, model_action_horizon)))
+        object.__setattr__(self, "_action_delta_indices_cache", (cache_key, indices))
+        return indices
 
     @property
     def reward_delta_indices(self) -> None:

src/lerobot/policies/groot/groot_n1_7.py

@@ -71,7 +71,7 @@ GR00T_N1_7_DEFAULTS: dict[str, Any] = {
     "backbone_embedding_dim": 2048,
     "tune_llm": False,
     "tune_visual": False,
-    "select_layer": 12,
+    "select_layer": 16,  # N1.7-3B checkpoint value; real checkpoint loads override this from config.json
     "reproject_vision": False,
     "use_flash_attention": True,
     "load_bf16": False,

src/lerobot/policies/groot/modeling_groot.py

@@ -23,6 +23,7 @@ orchestration are handled by LeRobot's standard training stack.
 """
 
 import builtins
+import logging
 import os
 from collections import deque
 from pathlib import Path
@@ -48,6 +49,8 @@ from .configuration_groot import (
     normalize_groot_model_version,
 )
 
+logger = logging.getLogger(__name__)
+
 T = TypeVar("T", bound="GrootPolicy")
 
 
@@ -149,9 +152,10 @@ 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)
@@ -182,7 +186,7 @@ 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,
@@ -198,7 +202,7 @@ 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
@@ -409,6 +413,11 @@ 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()}
 
@@ -471,33 +480,21 @@ 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")