OmniBrain-0: A Unified Agentic Model for Embodied Intelligence

Xiaomi Embodied Intelligence Team; Yuechen Luo; Chong He; Fang Li; Shaoqing Xu; Jingkai Wang; Menglun Su; Jiaxin Liu; Shengyin Jiang; Zhiyi Lai; Long Chen; Longlong Wang; Liang Hu; Haochen Liu; Yuncheng Jiang; Hongwei Xie; Jieming Cui; Hanbing Li; Junjie Huang; Zhixiang Duan; Hexin Zhang; Weixiang Liang; Ying Gong; Yuhan Wang; Yong Pan; Pengna Li; Kangyi Wu; Ken Deng; Kun Ma; Fuxi Wen; Zhi-xin Yang; Hangjun Ye

arXiv · 2610.36013

OmniBrain-0: A Unified Agentic Model for Embodied Intelligence

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Abstract

General-purpose embodied intelligence requires robots to coordinate diverse skills over long horizons, track task progress, and recover from failures. Specialist policies alone do not ensure task-level reliability, while powerful multimodal agents rely on costly reasoning and external experience that compact models struggle to reproduce. We introduce OmniBrain-0, a lightweight unified embodied agent that coordinates manipulation and navigation through a shared high-level decision model, integrating task understanding, planning, progress tracking, and feedback-driven recovery. Our progressive training framework combines embodied pretraining, domain-specialized agent distillation, and multi-teacher on-policy distillation (MOPD). It distills powerful teachers' interaction experience into manipulation and navigation experts, then consolidates their decision capabilities into OmniBrain-0. Unification therefore occurs within the learned decision model rather than only at the tool interface. We further introduce Rubric-Guided Recursive Self-Improvement (RSI), a data flywheel in which a Meta-Agent turns execution evidence into refined rubrics, step-level reminders, and verified trajectories for domain-expert updates before their reconsolidation into OmniBrain-0 through MOPD. We evaluate OmniBrain-0 on LIBERO-Pro, LIBERO-Plus, RoboCasa365, RoboTwin 2.0, and R2R-CE Val-Unseen, as well as multiple real-world manipulation tasks. OmniBrain-0 combines strong cross-domain task performance with substantially lower inference and execution overhead than its closed-source teacher, demonstrating efficient transfer and consolidation of embodied capabilities into a compact unified agent.

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Xiaomi Embodied Intelligence Team, Yuechen Luo, Chong He, Fang Li, Shaoqing Xu, Jingkai Wang, Menglun Su, Jiaxin Liu, Shengyin Jiang, Zhiyi Lai, Long Chen, Longlong Wang, Liang Hu, Haochen Liu, Yuncheng Jiang, Hongwei Xie, Jieming Cui, Hanbing Li, Junjie Huang, Zhixiang Duan, Hexin Zhang, Weixiang Liang, Ying Gong, Yuhan Wang, Yong Pan, Pengna Li, Kangyi Wu, Ken Deng, Kun Ma, Fuxi Wen, Zhi-xin Yang, Hangjun Ye. 2026-10-26. OmniBrain-0: A Unified Agentic Model for Embodied Intelligence. https://arxiv.org/abs/2610.36013

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