<a href=\"https://cdn-uploads.huggingface.co/production/uploads/65ec01fd770aa0e25d9374dc/l3RWk1XTh6hYamYr5K8Va.png\" rel=\"nofollow\"><img src=\"https://cdn-uploads.huggingface.co/production/uploads/65ec01fd770aa0e25d9374dc/l3RWk1XTh6hYamYr5K8Va.png\" alt=\"image\"></a></p>\n<p><a href=\"https://cdn-uploads.huggingface.co/production/uploads/65ec01fd770aa0e25d9374dc/__t2JWq2G3KATHZFaqpLB.png\" rel=\"nofollow\"><img src=\"https://cdn-uploads.huggingface.co/production/uploads/65ec01fd770aa0e25d9374dc/__t2JWq2G3KATHZFaqpLB.png\" alt=\"image\"></a></p>\n","updatedAt":"2026-05-18T06:02:46.984Z","author":{"_id":"65ec01fd770aa0e25d9374dc","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/65ec01fd770aa0e25d9374dc/yvLWwBEdAdHb-8EdUHg3n.jpeg","fullname":"Shijie Lian","name":"LiamLian0727","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":11,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.5497883558273315},"editors":["LiamLian0727"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/65ec01fd770aa0e25d9374dc/yvLWwBEdAdHb-8EdUHg3n.jpeg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2605.15298","authors":[{"_id":"6a0aaa5275184a0d71e02730","name":"Shijie Lian","hidden":false},{"_id":"6a0aaa5275184a0d71e02731","user":{"_id":"63d3b5f1640bb0f77173baea","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674819020331-noauth.jpeg","isPro":false,"fullname":"yubin","user":"VLyb","type":"user","name":"VLyb"},"name":"Bin Yu","status":"claimed_verified","statusLastChangedAt":"2026-05-18T07:46:18.138Z","hidden":false},{"_id":"6a0aaa5275184a0d71e02732","name":"Xiaopeng Lin","hidden":false},{"_id":"6a0aaa5275184a0d71e02733","name":"Changti Wu","hidden":false},{"_id":"6a0aaa5275184a0d71e02734","name":"Hang Yuan","hidden":false},{"_id":"6a0aaa5275184a0d71e02735","name":"Xiaolin Hu","hidden":false},{"_id":"6a0aaa5275184a0d71e02736","name":"Zhaolong Shen","hidden":false},{"_id":"6a0aaa5275184a0d71e02737","name":"Yuzhuo Miao","hidden":false},{"_id":"6a0aaa5275184a0d71e02738","name":"Haishan Liu","hidden":false},{"_id":"6a0aaa5275184a0d71e02739","name":"Yuxuan Tian","hidden":false},{"_id":"6a0aaa5275184a0d71e0273a","name":"Yukun Shi","hidden":false},{"_id":"6a0aaa5275184a0d71e0273b","name":"Cong Huang","hidden":false},{"_id":"6a0aaa5275184a0d71e0273c","name":"Kai Chen","hidden":false}],"publishedAt":"2026-05-14T00:00:00.000Z","submittedOnDailyAt":"2026-05-18T00:00:00.000Z","title":"PhysBrain 1.0 Technical Report","submittedOnDailyBy":{"_id":"65ec01fd770aa0e25d9374dc","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/65ec01fd770aa0e25d9374dc/yvLWwBEdAdHb-8EdUHg3n.jpeg","isPro":false,"fullname":"Shijie Lian","user":"LiamLian0727","type":"user","name":"LiamLian0727"},"summary":"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. 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