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OmniBrain-0: A Unified Agentic Model for Embodied Intelligence

Summary

Xiaomi's Embodied Intelligence Team introduces OmniBrain-0, a lightweight unified embodied agent that coordinates manipulation and navigation via a shared high-level decision model, trained with progressive distillation and recursive self-improvement.

SourcearXiv Computational LinguisticsAuthor: Xiaomi Embodied Intelligence Team, Yuechen Luo (University of Macau, Tsinghua University), Chong He (University of Macau, Tsinghua University), Fang Li (University of Macau, Tsinghua University), Shaoqing Xu (University of Macau, Tsinghua University), Jingkai Wang (University of Macau, Tsinghua University), Menglun Su (University of Macau, Tsinghua University), Jiaxin Liu (University of Macau, Tsinghua University), Shengyin Jiang (University of Macau, Tsinghua University), Zhiyi Lai (University of Macau, Tsinghua University), Long Chen (University of Macau, Tsinghua University), Longlong Wang (University of Macau, Tsinghua University), Liang Hu (University of Macau, Tsinghua University), Haochen Liu (University of Macau, Tsinghua University), Yuncheng Jiang (University of Macau, Tsinghua University), Hongwei Xie (University of Macau, Tsinghua University), Jieming Cui (University of Macau, Tsinghua University), Hanbing Li (University of Macau, Tsinghua University), Junjie Huang (University of Macau, Tsinghua University), Zhixiang Duan (University of Macau, Tsinghua University), Hexin Zhang (University of Macau, Tsinghua University), Weixiang Liang (University of Macau, Tsinghua University), Ying Gong (University of Macau, Tsinghua University), Yuhan Wang (University of Macau, Tsinghua University), Yong Pan (University of Macau, Tsinghua University), Pengna Li (University of Macau, Tsinghua University), Kangyi Wu (University of Macau, Tsinghua University), Ken Deng (University of Macau, Tsinghua University), Kun Ma (University of Macau, Tsinghua University), Fuxi Wen (University of Macau, Tsinghua University), Zhi-xin Yang (University of Macau, Tsinghua University), Hangjun Ye (University of Macau, Tsinghua University)
OmniBrain-0: A Unified Agentic Model for Embodied Intelligence
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[Submitted on 26 Oct 2026]

Title:OmniBrain-0: A Unified Agentic Model for Embodied Intelligence

View a PDF of the paper titled OmniBrain-0: A Unified Agentic Model for Embodied Intelligence, by Xiaomi Embodied Intelligence Team: Yuechen Luo and 31 other authors

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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.

Comments: Technical Report; 40 pages, 27 figures, 8 tables; Project Page at this https URL

Subjects:

Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2610.36013 [cs.CL]

(or arXiv:2610.36013v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.36013

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Jiaxin Liu [view email] [v1] Mon, 26 Oct 2026 17:15:41 UTC (42,491 KB)

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Key points and analysis

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Key points

  • Unifies task understanding, planning, progress tracking, and recovery within a single learned decision model rather than only at the tool interface.
  • Progressive training combines embodied pretraining, domain-specialized agent distillation, and multi-teacher on-policy distillation (MOPD).
  • Rubric-Guided Recursive Self-Improvement (RSI) uses a Meta-Agent to turn execution evidence into refined rubrics and verified trajectories.
  • Evaluated on LIBERO-Pro, LIBERO-Plus, RoboCasa365, RoboTwin 2.0, R2R-CE, and real-world tasks, achieving strong cross-domain performance with lower inference overhead than its closed-source teacher.

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