| title | PhysBrain 1.5: From Vision-Language Models to Physical Foundation Models |
|---|---|
| authors | DeepCybo Team, Yu Bin, Haipeng Cao, Zheng Chang, Kai Chen, Youning Chen, Kailin Deng, Yichao Du, Xiaotong Fu, Haoyang Ge, Yunlong Guo, Chenliu Hao, Jiyan He, Xuguo He, Yakun Hou, Kai Hu, Cong Huang, Tuopusen Huang, Yu Huang, Hong Li, Peize Li, Shijie Lian, Xiaopeng Lin, Yun Lin, Haibao Liu, Haochen Liu, Qiuzhi Liu, Shengcai Liu, Zhiqiang Liu, Tao Luo, Peng Ren, Shuo Ren, Chaoyi Ruan, Zhaolong Shen, Yukun Shi, Qiyuan Su, Yuxuan Tian, Yining Wang, Changti Wu, Hao Wu, Xueyin Xu, Ruoqi Yang, Zhaoyang Yang, Hang Yuan, Zhaoyang Zeng, Hanwen Zhang, Ruimeng Zhang, Yao Zhang, Yibo Zhang, Yuxiang Zhang, Zhirui Zhang, Ziyi Zhang, Zubin Zheng, Zishen Zhuang |
| date | 2026-09-14 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2609.14973 |
| affiliation | DeepCybo |
| upvotes | 64 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | We present PhysBrain 1.5, a unified model for understanding physical environments, generating actions, and predicting future states. Motivated by the physical loop of observation, interaction, and environmental change, we bring these capabilities into a common learning framework. Starting from a general vision--language model, we encode language responses, end-effector motion, and dense visual targets as discrete sequences and jointly optimize them with autoregressive next-token prediction. Pre-training draws its embodied supervision entirely from human interaction videos, using task-centered episodes to pair semantic and spatial context with recovered motion and subsequent observations. We then adapt the model through supervised fine-tuning on a mixture of human demonstrations, robot trajectories, and simulated experience. Across 28 embodied understanding benchmarks, our 8B model achieves an average score of 72.5, setting a new open-source state of the art and performing on par with leading proprietary models such as GPT-6-Astra and Gemini 3.6 Flash. It achieves the best open-source results on 14 benchmarks while retaining general multimodal capabilities. Beyond these understanding evaluations, qualitative examples show the model's ability to produce end-effector trajectories and predict future scenes through spatially aligned RGB, depth, and robot-mask outputs. |
cite
@misc{team2026physbrain,
title={PhysBrain 1.5: From Vision-Language Models to Physical Foundation Models},
author={DeepCybo Team and Yu Bin and Haipeng Cao and Zheng Chang and Kai Chen and Youning Chen and Kailin Deng and Yichao Du and Xiaotong Fu and Haoyang Ge and Yunlong Guo and Chenliu Hao and Jiyan He and Xuguo He and Yakun Hou and Kai Hu and Cong Huang and Tuopusen Huang and Yu Huang and Hong Li and Peize Li and Shijie Lian and Xiaopeng Lin and Yun Lin and Haibao Liu and Haochen Liu and Qiuzhi Liu and Shengcai Liu and Zhiqiang Liu and Tao Luo and Peng Ren and Shuo Ren and Chaoyi Ruan and Zhaolong Shen and Yukun Shi and Qiyuan Su and Yuxuan Tian and Yining Wang and Changti Wu and Hao Wu and Xueyin Xu and Ruoqi Yang and Zhaoyang Yang and Hang Yuan and Zhaoyang Zeng and Hanwen Zhang and Ruimeng Zhang and Yao Zhang and Yibo Zhang and Yuxiang Zhang and Zhirui Zhang and Ziyi Zhang and Zubin Zheng and Zishen Zhuang},
year={2026},
eprint={2609.14973},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2609.14973}
}