PhysBrain 1.0 Technical Report
Vision-language-action models have advanced rapidly, but robot trajectories alone provide limited coverage for learning broad physical understanding. PhysBrain 1.0 studies a complementary route: converting large-scale human egocentric video into structured physical commonsense supervision before robot adaptation. Our data engine extracts scene elements, spatial dynamics, action execution, and depth-aware relations, then turns them into question-answer supervision for training PhysBrain VLMs. The resulting physical priors are further transferred to VLA policies through a capability-preserving and language-sensitive adaptation design. Across multimodal QA benchmarks and embodied control ben...
| Authors | Shijie Lian, Bin Yu, Xiaopeng Lin et al. |
|---|---|
| Primary category | cs.RO |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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