RoboOS: A Hierarchical Embodied Framework for Cross-Embodiment and Multi-Agent Collaboration (2505.03673v2)

Abstract: 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

RoboOS: Advancements in Hierarchical Multi-Agent Embodied Intelligence

The paper "RoboOS: A Hierarchical Embodied Framework for Cross-Embodiment and Multi-Agent Collaboration" describes a novel framework aimed at revolutionizing embodied intelligence through multi-agent collaboration across various industrial settings, including manufacturing, service robotics, and cyber-physical systems. The framework addresses existing limitations in robotic systems, notably the inefficiencies in cross-embodiment adaptability, task scheduling, and dynamic error correction. RoboOS introduces a hierarchical architecture dubbed the Brain-Cerebellum model that enhances these domains.

Framework Components

RoboOS consists of three primary components that together facilitate robust planning, execution, and coordination:

1. Embodied Brain Model (RoboBrain): This component employs a multimodal LLM (MLLM) for perceptive and high-level decision-making tasks. It orchestrates global perception and integrates environmental cues for effective task decomposition and trajectory prediction.
2. Cerebellum Skill Library: A modular toolkit designed for seamless execution of diverse robotic tasks. This component supports heterogeneous embodiments—ranging from single-arm systems to full humanoid and wheeled robots—and offers functionalities such as manipulation and navigation.
3. Real-Time Shared Memory: Serving as the synchronization hub, it maintains spatial and temporal memory, facilitating coherent multi-agent states for effective collaboration and error correction.

Performance and Results

The authors conducted extensive experiments in real-world scenarios such as restaurants, households, and supermarkets. These experiments showcase the versatility of RoboOS across various robotic embodiments, underscoring its capability in ensuring scalable and adaptive multi-agent collaboration. For instance, in a collaborative task depicted as "apple-and-knife delivery," RoboOS dynamically allocates subtasks to different robots, showcasing its ability to manage complex workflows and enhance operational interaction between robots with varied functionalities.

Implications and Future Directions

The paper suggests significant implications of RoboOS in advancing the scope of embodied intelligence, offering scalable solutions that extend beyond industrial automation into areas like general service robotics and adaptive production architectures. The proposed architecture encourages future research in exploring more complex collaborative settings involving multiple robots, which may lead to significant advancements in AI-driven robotics systems.

There is potential for improvement in edge-cloud communication methodologies, particularly concerning data sharing and real-time decision-making, which could further bolster the deployment efficiency of RoboOS in industrial settings.

Conclusion

RoboOS exemplifies a significant methodological advancement in hierarchical multi-agent intelligence systems, demonstrating robust applicability in task collaboration and adaptability across diversified robotic embodiments. The framework’s comprehensive approach in overcoming existing limitations points to its transformative potential in embodied AI, setting the stage for future exploration into even more complex bodily and cognitive tasks, broadening the horizons of robotics collaborations in industrial contexts.