Add Wall-X serving and Turtle2 TCP WebSocket bridge

wall_x/_vendor/harrix/utils/normalizer.py

@@ -1,8 +1,10 @@
 """Build action/proprio normalizers and resolve the effective norm key.
 
 Public inference artifacts must carry their own normalization data. This module
-uses checkpoint-local ``norm_stats.json`` first, then checkpoint-side normalizer
-state dicts, and finally an explicit ``customized_action_statistic_dof`` path.
+uses checkpoint-local ``norm_stats.json`` for the 7D Euler layout, and uses
+checkpoint-side normalizer state dicts for checkpoints whose train config contains
+6D rotation fields. It finally supports an explicit
+``customized_action_statistic_dof`` path.
 It does not fall back to internal default action statistics.
 """
 
@@ -59,6 +61,19 @@ def _missing_normalizer_error(checkpoint_path: str, train_config: dict) -> FileN
     )
 
 
+def _uses_rotation_6d(train_config: dict) -> bool:
+    """Return whether the checkpoint was trained with 6D rotation fields."""
+    for layout_name in ("dof_config", "agent_pos_config", "ar_dof_config"):
+        layout = train_config.get(layout_name)
+        if layout is None and isinstance(train_config.get("task"), dict):
+            layout = train_config["task"].get(layout_name)
+        if isinstance(layout, dict) and any(
+            "rotation_6d" in str(key).lower() for key in layout
+        ):
+            return True
+    return False
+
+
 def build_normalizers(
     checkpoint_path: str,
     train_config: dict,
@@ -66,14 +81,24 @@ def build_normalizers(
 ) -> tuple[Normalizer, Normalizer, str]:
     """Return action/proprio normalizers and the resolved norm key."""
     norm_stats_path = os.path.join(checkpoint_path, "norm_stats.json")
-    if os.path.exists(norm_stats_path):
+    action_pth = os.path.join(checkpoint_path, "normalizer_action.pth")
+    propri_pth = os.path.join(checkpoint_path, "normalizer_propri.pth")
+    prefer_checkpoint_pth = (
+        os.path.exists(action_pth)
+        and os.path.exists(propri_pth)
+        and _uses_rotation_6d(train_config)
+    )
+    if os.path.exists(norm_stats_path) and not prefer_checkpoint_pth:
         propri_stats = _load_norm_stats(norm_stats_path, "observation.state")
         action_stats = _load_norm_stats(norm_stats_path, "action")
         normalizer_propri = Normalizer.from_lerobot_norm_stats(propri_stats, norm_key)
         normalizer_action = Normalizer.from_lerobot_norm_stats(action_stats, norm_key)
     else:
-        action_pth = os.path.join(checkpoint_path, "normalizer_action.pth")
-        propri_pth = os.path.join(checkpoint_path, "normalizer_propri.pth")
+        if prefer_checkpoint_pth:
+            logger.info(
+                "Using checkpoint normalizer .pth files because train config "
+                "contains rotation_6D; ignoring 7D norm_stats.json"
+            )
         custom_stats = _load_custom_action_stats(train_config)
         if custom_stats is None and (not os.path.exists(action_pth) or not os.path.exists(propri_pth)):
             raise _missing_normalizer_error(checkpoint_path, train_config)