# PhysBrain 1.0 Technical Report

Page: https://stenobird.com/podcast/daily-paper-cast-7079649/physbrain-1-0-technical-report
Text version: https://stenobird.com/podcast/daily-paper-cast-7079649/physbrain-1-0-technical-report.md
Podcast: [Daily Paper Cast](https://stenobird.com/podcast/daily-paper-cast-7079649)
Published: 2026-05-19T04:21:38+00:00
Episode link: https://share.transistor.fm/s/053171c2
Audio file: https://media.transistor.fm/053171c2/ba4315b5.mp3
Processing state: not_requested
JSON: https://stenobird.com/v1/public/podcasts/daily-paper-cast-7079649/episodes/physbrain-1-0-technical-report
Duration seconds: 1524

## Resource

🤗 Upvotes: 131 | cs.RO, cs.AI, cs.CL, cs.CV Authors: Shijie Lian, Bin Yu, Xiaopeng Lin, Changti Wu, Hang Yuan, Xiaolin Hu, Zhaolong Shen, Yuzhuo Miao, Haishan Liu, Yuxuan Tian, Yukun Shi, Cong Huang, Kai Chen Title: PhysBrain 1.0 Technical Report Arxiv: http://arxiv.org/abs/2605.15298v1 Abstract: 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.

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