fix(smolvla2): tokenize lang prompt inline before select_action

LowLevelForward was handing the observation provider's output straight
to ``policy.select_action``, but SmolVLA's ``_get_action_chunk``
indexes ``batch[OBS_LANGUAGE_TOKENS]`` and crashes with ``KeyError:
'observation.language.tokens'`` when the key isn't there. Our provider
deliberately strips the dataset's language columns (the runtime drives
messages itself), so nothing else was producing those tokens — the
chunk path crashed on the very first tick after task was set.

Build a low-level prompt from current runtime state inline (task /
plan / memory as the user turn, current subtask appended as a
continuation assistant turn when known), tokenize it with the same
helper the high-level steps use, and merge ``lang_tokens`` /
``lang_masks`` into the observation before the call. Skip the step
when no task is set yet, and swallow ``select_action`` exceptions at
debug level so a missing observation feature doesn't kill the REPL.

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

src/lerobot/policies/smolvla2/inference/steps.py

@@ -91,10 +91,35 @@ class LowLevelForward(InferenceStep):
     def run(self, state: dict[str, Any]) -> dict[str, Any] | None:
         if self.policy is None or self.observation_provider is None:
             return None
+        if not state.get("task"):
+            # No task yet → nothing useful to condition on.
+            return None
         observation = self.observation_provider()
         if observation is None:
             return None
-        action = self.policy.select_action(observation)
+        # SmolVLA's ``select_action`` expects the full preprocessed
+        # batch, including ``OBS_LANGUAGE_TOKENS`` /
+        # ``OBS_LANGUAGE_ATTENTION_MASK``. The observation provider
+        # only returns image / state features (the runtime drives
+        # messages itself), so build a low-level prompt from current
+        # runtime state and tokenize it inline.
+        ctx = _control_context_messages(state)
+        if state.get("current_subtask"):
+            ctx = ctx + [{"role": "assistant", "content": state["current_subtask"]}]
+        text_batch = _build_text_batch(self.policy, ctx)
+        from lerobot.utils.constants import (  # noqa: PLC0415
+            OBS_LANGUAGE_ATTENTION_MASK,
+            OBS_LANGUAGE_TOKENS,
+        )
+
+        observation = dict(observation)
+        observation[OBS_LANGUAGE_TOKENS] = text_batch["lang_tokens"]
+        observation[OBS_LANGUAGE_ATTENTION_MASK] = text_batch["lang_masks"]
+        try:
+            action = self.policy.select_action(observation)
+        except Exception as exc:  # noqa: BLE001
+            logger.debug("select_action skipped: %s", exc)
+            return None
         # SmolVLA returns a single action; if the underlying policy
         # streams chunks, split per-step here. For v1 we just enqueue
         # the result.