fix(smolvla2): wrap robot obs in EnvTransition before preprocessor

The policy preprocessor pipeline is transition-shaped — its steps
read ``TransitionKey.OBSERVATION`` off an ``EnvTransition`` dict, not
a flat ``RobotObservation`` dict. Passing the raw observation through
made every step bail with
``ObservationProcessorStep requires an observation in the transition``,
which the runtime swallowed at warning level. ``select_message`` then
got called with no ``observation.images.*`` features and crashed
with ``All image features are missing from the batch``.

Mirror ``lerobot-record``'s preamble:
  1. ``prepare_observation_for_inference`` → numpy → torch, ``CHW``
     image layout, ``[0,1]`` scaling, add batch dim, move to device.
  2. Wrap into an ``EnvTransition`` (``{TransitionKey.OBSERVATION.value:
     ...}`` plus ``COMPLEMENTARY_DATA: {}`` and ``None``s for the rest)
     so transition-aware steps see the keys they expect.
  3. Run preprocessor.
  4. Unwrap the transition's ``OBSERVATION`` slot to get the final
     flat dict the policy's ``select_action`` / ``select_message``
     consume.

Image features now reach the policy; the autonomous loop produces
real actions instead of swallowing warnings every tick.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

src/lerobot/scripts/lerobot_smolvla2_runtime.py

@@ -570,14 +570,25 @@ def _build_robot_observation_provider(
 ) -> Callable[[], dict | None]:
     """Closure that reads from the robot, runs the policy preprocessor.
 
-    Each call: ``robot.get_observation()`` → wrap as a flat sample dict
-    → drop language columns (the runtime drives messages itself) →
-    preprocessor (rename, batch dim, normalise, device-place) → return
-    the observation batch ready for ``policy.select_action`` and
-    ``policy.select_message``.
+    Each call: ``robot.get_observation()`` (raw numpy dict) →
+    ``prepare_observation_for_inference`` (tensor / batch dim / device) →
+    wrap in an ``EnvTransition`` (the preprocessor pipeline is
+    transition-shaped, keyed by ``TransitionKey``) → preprocessor
+    (rename, render-messages no-op when no language columns, chat
+    tokenizer no-op when no messages, normalise) → unwrap and return
+    the flat observation batch ``policy.select_action`` /
+    ``policy.select_message`` consume.
     """
     import torch  # noqa: PLC0415
 
+    from lerobot.policies.utils import prepare_observation_for_inference  # noqa: PLC0415
+    from lerobot.types import TransitionKey  # noqa: PLC0415
+
+    torch_device = torch.device(device) if isinstance(device, str) else device
+    robot_type = getattr(robot, "robot_type", None) or getattr(
+        getattr(robot, "config", None), "type", None
+    )
+
     def _provider() -> dict | None:
         try:
             raw = robot.get_observation()
@@ -585,30 +596,44 @@ def _build_robot_observation_provider(
             logger.warning("robot.get_observation failed: %s", exc)
             return None
 
-        sample: dict[str, Any] = dict(raw)
-        if task:
-            sample.setdefault("task", task)
-        # The render step expects either both language columns or
-        # neither — runtime supplies messages itself, so make sure
-        # nothing leaks through.
+        # Strip language-column leakage just in case (the runtime
+        # supplies messages itself).
         for k in ("language_persistent", "language_events"):
-            sample.pop(k, None)
+            raw.pop(k, None)
+
+        try:
+            obs_tensors = prepare_observation_for_inference(
+                raw, torch_device, task=task, robot_type=robot_type
+            )
+        except Exception as exc:  # noqa: BLE001
+            logger.warning("prepare_observation_for_inference failed: %s", exc)
+            return None
 
         if preprocessor is not None:
+            transition: dict[str, Any] = {
+                TransitionKey.OBSERVATION.value: obs_tensors,
+                TransitionKey.ACTION.value: None,
+                TransitionKey.REWARD.value: None,
+                TransitionKey.DONE.value: None,
+                TransitionKey.TRUNCATED.value: None,
+                TransitionKey.INFO.value: None,
+                TransitionKey.COMPLEMENTARY_DATA.value: {},
+            }
             try:
-                sample = preprocessor(sample)
+                transition = preprocessor(transition)
             except Exception as exc:  # noqa: BLE001
                 logger.warning("preprocessor failed on robot observation: %s", exc)
                 return None
+            obs_tensors = transition.get(TransitionKey.OBSERVATION.value) or {}
 
         observation = {
             k: v
-            for k, v in sample.items()
+            for k, v in obs_tensors.items()
             if isinstance(k, str) and k.startswith("observation.")
         }
         for k, v in list(observation.items()):
             if isinstance(v, torch.Tensor):
-                observation[k] = v.to(device)
+                observation[k] = v.to(torch_device)
         return observation
 
     return _provider