feat(smolvla2): startup task picker, /vlm mode toggle, interactive VQA overlay

Three additions to the SmolVLA2 interactive runtime:

1. Startup task picker — when no --task is given, the runtime lists the
   dataset's task strings as a numbered menu (plus a custom-task option)
   instead of silently waiting for the first stdin line.

2. Mode toggle — /action and /vlm slash commands flip a persistent run
   mode. /vlm pauses the whole action loop (HighLevelSubtaskFwd,
   LowLevelForward and DispatchAction gate on state["mode"]) and clears
   the action queue so the robot holds position; /action resumes it.
   The mode is shown in the state panel.

3. Interactive VQA — in /vlm mode a typed line is a VQA question. The
   new inference/vqa.py module asks which camera to ground on, runs the
   VLM on that single camera, and when the answer is a bbox/keypoint it
   draws the overlay, saves a PNG to ./vqa_overlays/ and auto-opens it.

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

tests/policies/smolvla/test_smolvla2_vqa_overlay.py

@@ -0,0 +1,179 @@
+#!/usr/bin/env python
+
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Tests for the SmolVLA2 runtime's interactive-VQA helpers.
+
+Covers camera selection, VQA-answer parsing, and the bounding-box /
+keypoint overlay drawing — the pure functions, no model load.
+"""
+
+import numpy as np
+import pytest
+
+from lerobot.policies.smolvla2.inference.vqa import (
+    answer_has_overlay,
+    available_cameras,
+    camera_short_name,
+    draw_vqa_overlay,
+    observation_image_to_pil,
+    parse_vqa_answer,
+    prompt_camera_choice,
+)
+
+PIL = pytest.importorskip("PIL")
+from PIL import Image  # noqa: E402
+
+# ---------------------------------------------------------------------------
+# Camera selection
+# ---------------------------------------------------------------------------
+
+
+def test_available_cameras_extracts_and_sorts_image_keys():
+    observation = {
+        "observation.images.wrist": object(),
+        "observation.state": object(),
+        "observation.images.top": object(),
+        "task": "x",
+    }
+    assert available_cameras(observation) == [
+        "observation.images.top",
+        "observation.images.wrist",
+    ]
+
+
+def test_available_cameras_handles_none_and_empty():
+    assert available_cameras(None) == []
+    assert available_cameras({}) == []
+
+
+def test_camera_short_name_strips_prefix():
+    assert camera_short_name("observation.images.top") == "top"
+    assert camera_short_name("top") == "top"
+
+
+def test_prompt_camera_choice_single_camera_auto_selects():
+    cams = ["observation.images.top"]
+    # input_fn must never be called for a single-camera setup.
+    chosen = prompt_camera_choice(cams, input_fn=_boom, print_fn=lambda *_: None)
+    assert chosen == "observation.images.top"
+
+
+def test_prompt_camera_choice_by_number():
+    cams = ["observation.images.top", "observation.images.wrist"]
+    chosen = prompt_camera_choice(cams, input_fn=lambda _: "2", print_fn=lambda *_: None)
+    assert chosen == "observation.images.wrist"
+
+
+def test_prompt_camera_choice_by_name():
+    cams = ["observation.images.top", "observation.images.wrist"]
+    chosen = prompt_camera_choice(cams, input_fn=lambda _: "top", print_fn=lambda *_: None)
+    assert chosen == "observation.images.top"
+
+
+def test_prompt_camera_choice_invalid_returns_none():
+    cams = ["observation.images.top", "observation.images.wrist"]
+    assert prompt_camera_choice(cams, input_fn=lambda _: "99", print_fn=lambda *_: None) is None
+
+
+def _boom(*_args, **_kwargs):
+    raise AssertionError("input_fn should not be called")
+
+
+# ---------------------------------------------------------------------------
+# Answer parsing
+# ---------------------------------------------------------------------------
+
+
+def test_parse_bbox_answer():
+    answer = '{"detections": [{"label": "cube", "bbox_format": "xyxy", "bbox": [10, 20, 50, 80]}]}'
+    parsed = parse_vqa_answer(answer)
+    assert parsed["kind"] == "bbox"
+    assert answer_has_overlay(parsed)
+
+
+def test_parse_keypoint_answer():
+    answer = '{"label": "blue cube", "point_format": "xy", "point": [120, 90]}'
+    parsed = parse_vqa_answer(answer)
+    assert parsed["kind"] == "keypoint"
+    assert answer_has_overlay(parsed)
+
+
+def test_parse_count_answer_is_not_an_overlay():
+    parsed = parse_vqa_answer('{"label": "cubes", "count": 2}')
+    assert parsed["kind"] == "count"
+    assert not answer_has_overlay(parsed)
+
+
+def test_parse_invalid_json_returns_none():
+    assert parse_vqa_answer("not json at all") is None
+    assert parse_vqa_answer("") is None
+    # A JSON array is valid JSON but not a VQA answer object.
+    assert parse_vqa_answer("[1, 2, 3]") is None
+
+
+def test_parse_unknown_shape():
+    parsed = parse_vqa_answer('{"weird": "payload"}')
+    assert parsed["kind"] == "unknown"
+    assert not answer_has_overlay(parsed)
+
+
+# ---------------------------------------------------------------------------
+# Overlay drawing
+# ---------------------------------------------------------------------------
+
+
+def _blank(size=(160, 120)):
+    return Image.new("RGB", size, (0, 0, 0))
+
+
+def test_draw_bbox_overlay_changes_pixels_and_preserves_size():
+    img = _blank()
+    parsed = parse_vqa_answer(
+        '{"detections": [{"label": "cube", "bbox_format": "xyxy", "bbox": [10, 20, 50, 80]}]}'
+    )
+    out = draw_vqa_overlay(img, parsed)
+    assert out.size == img.size
+    assert out.tobytes() != img.tobytes()
+
+
+def test_draw_keypoint_overlay_changes_pixels():
+    img = _blank()
+    parsed = parse_vqa_answer('{"label": "cube", "point_format": "xy", "point": [80, 60]}')
+    out = draw_vqa_overlay(img, parsed)
+    assert out.size == img.size
+    assert out.tobytes() != img.tobytes()
+
+
+def test_draw_overlay_non_spatial_leaves_image_unchanged():
+    img = _blank()
+    parsed = parse_vqa_answer('{"label": "cubes", "count": 2}')
+    out = draw_vqa_overlay(img, parsed)
+    assert out.tobytes() == img.tobytes()
+
+
+def test_draw_overlay_tolerates_malformed_coordinates():
+    img = _blank()
+    # bbox with the wrong arity must not raise.
+    out = draw_vqa_overlay(img, {"kind": "bbox", "payload": {"detections": [{"bbox": [1, 2]}]}})
+    assert out.size == img.size
+
+
+def test_observation_image_to_pil_from_batched_float_array():
+    # (1, C, H, W) float array in [0, 1], the runtime observation shape.
+    arr = np.zeros((1, 3, 24, 32), dtype=np.float32)
+    pil = observation_image_to_pil(arr)
+    assert pil.size == (32, 24)
+    assert pil.mode == "RGB"