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Pepijn
47fb8318b1
The tensor-level comparison between dry-run (dataset frame) and live- robot inference proved the runtime is bug-free — same shape, dtype, device, channel order, batch dim, and normalization on both paths. The remaining variable: front-camera mean brightness was 0.26 live vs 0.39 on the dataset frame, ~33% darker. Training augmentation only covered ±20% brightness, so the live scene sits just outside the supervised envelope and the LM head collapses to its dominant prior. Widen the augmentation knobs for the next retrain: * brightness 0.8–1.2 → 0.5–1.6 (covers ~30% darker / 60% lighter) * contrast 0.8–1.2 → 0.6–1.5 * saturation 0.5–1.5 → 0.3–1.7 * hue ±0.05 → ±0.10 * affine ±5°/±5% → ±15°/±15% (covers cube placement / camera drift) * max_num_transforms 3 → 4 And bump prompt-component dropout (subtask 0.20 → 0.30) so the LM can't lean on stale memorised plan/memory at inference. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>