Paper Detail
Haotian Liang, Mingkang Chen, Yufei Huang, Yuchun Guo, Xiaomeng Zhu, Xiangli Shi, Kaixuan Wang, Yunxuan Mao, Weijie Zhou, Ling Chen, Shirong Zeng, Yueyu Long, Yuchen Si, Yajuan Zhu, Xingyu Zhou, Minghui Wang, Wanjia He, Xin Yang, Lingzhu Xiang, Zhiqing Liu, Bohan Ma, Xiran Huang, Tianshuo Yang, Zhiheng Liu, Xuantang Xiong, Zisheng Lu, Ping Luo, Yao Mu, Han Hu, Zhengyou Zhang
Embodied cognition requires agents to connect high-level task reasoning with the physical states to be achieved. We introduce Hy-Embodied-RxBrain, an embodied cognition foundation model with joint language-visual reasoning and imagination. Unlike vision-language models that emphasize scene understanding and textual decision making, or generative world models that mainly predict future visual states, RxBrain represents embodied plans in a single planning sequence where language and visual imagination play complementary roles. Language provides the abstract structure of a plan, including task decomposition, planning primitives, constraints, temporal order, and decision logic, while visual imagination grounds this structure through world state prediction and joint subgoal planning, associating each planning step with intermediate and final physical states. RxBrain adopts a unified multimodal Mixture-of-Transformers architecture that supports language, image, and video understanding and generation within one model. To train this capability, we build an automatic pipeline that converts embodied videos into joint text-visual planning supervision by decomposing videos into planning steps and aligning them with visual state transitions. We further introduce RxBrain-Bench to evaluate whether models can represent embodied plans through joint textual and visual components rather than separate understanding or generation. Experiments show that RxBrain maintains embodied understanding and generation abilities, and produces plans with coupled textual reasoning, world state prediction, and joint subgoal planning. We also extend RxBrain to continuous robot action generation, where it shows promising real-robot performance without large-scale action-data pretraining. These results provide an initial step toward foundation models for embodied cognition.
No structured notes yet. Add `summary_sections`, `why_relevant`, `claim_impact`, or `next_action` in `papers.jsonl` to enrich this view.
No ranking explanation is available yet.
No tags.
@misc{liang2026rxbrain,
title = {RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination},
author = {Haotian Liang and Mingkang Chen and Yufei Huang and Yuchun Guo and Xiaomeng Zhu and Xiangli Shi and Kaixuan Wang and Yunxuan Mao and Weijie Zhou and Ling Chen and Shirong Zeng and Yueyu Long and Yuchen Si and Yajuan Zhu and Xingyu Zhou and Minghui Wang and Wanjia He and Xin Yang and Lingzhu Xiang and Zhiqing Liu and Bohan Ma and Xiran Huang and Tianshuo Yang and Zhiheng Liu and Xuantang Xiong and Zisheng Lu and Ping Luo and Yao Mu and Han Hu and Zhengyou Zhang},
year = {2026},
abstract = {Embodied cognition requires agents to connect high-level task reasoning with the physical states to be achieved. We introduce Hy-Embodied-RxBrain, an embodied cognition foundation model with joint language-visual reasoning and imagination. Unlike vision-language models that emphasize scene understanding and textual decision making, or generative world models that mainly predict future visual states, RxBrain represents embodied plans in a single planning sequence where language and visual imagina},
url = {https://huggingface.co/papers/2607.14187},
keywords = {code available, huggingface daily},
eprint = {2607.14187},
archiveprefix = {arXiv},
}
{}