diff --git a/src/lerobot/scripts/lerobot_smolvla2_runtime.py b/src/lerobot/scripts/lerobot_smolvla2_runtime.py
index c92f26a84..f59120936 100644
--- a/src/lerobot/scripts/lerobot_smolvla2_runtime.py
+++ b/src/lerobot/scripts/lerobot_smolvla2_runtime.py
@@ -567,21 +567,27 @@ def _build_robot_observation_provider(
     preprocessor: Any,
     device: str,
     task: str | None,
+    ds_features: dict[str, Any] | None,
 ) -> Callable[[], dict | None]:
     """Closure that reads from the robot, runs the policy preprocessor.
 
-    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.
+    Each call: ``robot.get_observation()`` (raw per-joint + per-camera
+    dict, possibly with scalar floats) → ``build_inference_frame``
+    (extract the keys the dataset declared, reshape per-joint floats
+    into a single ``observation.state`` vector, prefix camera keys
+    with ``observation.images.``, convert to tensors with batch dim
+    on device) → wrap in an ``EnvTransition`` (the preprocessor
+    pipeline is transition-shaped, keyed by ``TransitionKey``) →
+    preprocessor (rename, 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.policies.utils import (  # noqa: PLC0415
+        build_inference_frame,
+        prepare_observation_for_inference,
+    )
     from lerobot.types import TransitionKey  # noqa: PLC0415
 
     torch_device = torch.device(device) if isinstance(device, str) else device
@@ -602,11 +608,24 @@ def _build_robot_observation_provider(
             raw.pop(k, None)
 
         try:
-            obs_tensors = prepare_observation_for_inference(
-                raw, torch_device, task=task, robot_type=robot_type
-            )
+            if ds_features:
+                # Use the dataset's feature schema to pick the right
+                # raw keys and fold per-joint scalars into a single
+                # ``observation.state`` tensor. Then tensor-ise +
+                # device-place + add batch dim.
+                obs_tensors = build_inference_frame(
+                    raw, torch_device, ds_features=ds_features,
+                    task=task, robot_type=robot_type,
+                )
+            else:
+                # No dataset features available — fall back to the
+                # generic numpy-only path; only works when the robot
+                # already returns dataset-shaped keys.
+                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)
+            logger.warning("observation prep failed: %s", exc)
             return None
 
         if preprocessor is not None:
@@ -869,6 +888,7 @@ def main(argv: list[str] | None = None) -> int:
             preprocessor=preprocessor,
             device=str(getattr(policy.config, "device", "cpu")),
             task=args.task,
+            ds_features=ds_meta.features if ds_meta is not None else None,
         )
         robot_executor = _build_robot_action_executor(
             robot=robot,