Spatial VQA answers (bbox / keypoint) were trained as pixel-coordinate
JSON, which fights PaliGemma's detection prior and leaks <loc>-token
salad at inference. Convert them to PaliGemma's native <locNNNN>
vocabulary instead so the LM head reuses that prior.
Training side (text_processor_pi052.py): a target turn whose content
parses as a bbox/keypoint answer is rewritten to <loc> text, using the
camera frame's native (H, W) from the observation and the preceding
image block. Non-spatial answers, subtask/memory targets and SmolVLA2
keep their JSON form — the dataset stays backbone-agnostic.
Runtime side (smolvla2/inference/vqa.py): parse_vqa_answer detects
<loc> answers (2 locs -> keypoint, 4 -> bbox), returning normalized
[0,1] coords with a normalized flag; draw_vqa_overlay denormalizes
against the chosen camera frame's pixel size.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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>
Pepijn