Kavli Affiliate: Cheng Peng
| First 5 Authors: Huajie Tan, Xiaoshuai Hao, Cheng Chi, Minglan Lin, Yaoxu Lyu
| Summary:
The dawn of embodied intelligence has ushered in an unprecedented imperative
for resilient, cognition-enabled multi-agent collaboration across
next-generation ecosystems, revolutionizing paradigms in autonomous
manufacturing, adaptive service robotics, and cyber-physical production
architectures. However, current robotic systems face significant limitations,
such as limited cross-embodiment adaptability, inefficient task scheduling, and
insufficient dynamic error correction. While End-to-end VLA models demonstrate
inadequate long-horizon planning and task generalization, hierarchical VLA
models suffer from a lack of cross-embodiment and multi-agent coordination
capabilities. To address these challenges, we introduce RoboOS, the first
open-source embodied system built on a Brain-Cerebellum hierarchical
architecture, enabling a paradigm shift from single-agent to multi-agent
intelligence. Specifically, RoboOS consists of three key components: (1)
Embodied Brain Model (RoboBrain), a MLLM designed for global perception and
high-level decision-making; (2) Cerebellum Skill Library, a modular,
plug-and-play toolkit that facilitates seamless execution of multiple skills;
and (3) Real-Time Shared Memory, a spatiotemporal synchronization mechanism for
coordinating multi-agent states. By integrating hierarchical information flow,
RoboOS bridges Embodied Brain and Cerebellum Skill Library, facilitating robust
planning, scheduling, and error correction for long-horizon tasks, while
ensuring efficient multi-agent collaboration through Real-Time Shared Memory.
Furthermore, we enhance edge-cloud communication and cloud-based distributed
inference to facilitate high-frequency interactions and enable scalable
deployment. Extensive real-world experiments across various scenarios,
demonstrate RoboOS’s versatility in supporting heterogeneous embodiments.
Project website: https://github.com/FlagOpen/RoboOS
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