Merge branch 'feat/add_relative_action_pi_models' into feat/mirror

src/lerobot/processor/normalize_processor.py

@@ -331,11 +331,9 @@ class _NormalizationMixin:
                 )
 
             mean, std = stats["mean"], stats["std"]
-            # Avoid division by zero by adding a small epsilon.
-            denom = std + self.eps
             if inverse:
-                return tensor * std + mean
-            return (tensor - mean) / denom
+                return tensor * (std + 1e-6) + mean
+            return (tensor - mean) / (std + 1e-6)
 
         if norm_mode == NormalizationMode.MIN_MAX:
             min_val = stats.get("min", None)
@@ -367,11 +365,7 @@ class _NormalizationMixin:
                     "QUANTILES normalization mode requires q01 and q99 stats, please update the dataset with the correct stats using the `augment_dataset_quantile_stats.py` script"
                 )
 
-            denom = q99 - q01
-            # Avoid division by zero by adding epsilon when quantiles are identical
-            denom = torch.where(
-                denom == 0, torch.tensor(self.eps, device=tensor.device, dtype=tensor.dtype), denom
-            )
+            denom = q99 - q01 + 1e-6
             if inverse:
                 return (tensor + 1.0) * denom / 2.0 + q01
             return 2.0 * (tensor - q01) / denom - 1.0

src/lerobot/scripts/lerobot_train.py

@@ -284,10 +284,22 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
         all_delta = np.concatenate(all_delta_actions, axis=0)
         delta_stats = get_feature_stats(all_delta, axis=0, keepdims=all_delta.ndim == 1)
         dataset.meta.stats["action"] = delta_stats
+
+        # Determine normalization type for logging
+        norm_type = "UNKNOWN"
+        if hasattr(cfg.policy, "normalization_mapping"):
+            from lerobot.configs.types import NormalizationMode
+            action_norm = cfg.policy.normalization_mapping.get("ACTION", None)
+            norm_type = action_norm.value if action_norm else "UNKNOWN"
+
         logging.info(
-            f"Delta action stats: mean={np.abs(delta_stats['mean']).mean():.4f}, "
-            f"std={delta_stats['std'].mean():.4f}"
+            f"Delta action stats ({len(all_delta_actions)} chunks, {len(all_delta)} values, norm={norm_type}): "
+            f"mean={np.abs(delta_stats['mean']).mean():.4f}, std={delta_stats['std'].mean():.4f}, "
+            f"q01={delta_stats['q01'].mean():.4f}, q99={delta_stats['q99'].mean():.4f}"
         )
+        if norm_type == "QUANTILES":
+            q_range = (delta_stats['q99'] - delta_stats['q01']).mean()
+            logging.info(f"  Quantile range (q99-q01): {q_range:.4f}")
 
     # Wait for all processes to finish policy creation before continuing
     accelerator.wait_for_everyone()