PhysBrain 1.0 Technical Report
Authors: name: Shijie Lian, name: Bin Yu, name: Xiaopeng Lin, name: Changti Wu, name: Hang Yuan, name: Xiaolin Hu, name: Zhaolong Shen, name: Yuzhuo Miao, name: Haishan Liu, name: Yuxuan Tian, name: Yukun Shi, name: Cong Huang, name: Kai Chen
Organization: DeepCybo

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 benchmarks, including ERQA, PhysBench, SimplerEnv-WidowX, LIBERO, and RoboCasa, PhysBrain 1.0 achieves SOTA results and shows especially strong out-of-domain performance on SimplerEnv. These results suggest that scaling physical commonsense from human interaction video can provide an effective bridge from multimodal understanding to robot action.