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RoboOS: A Hierarchical Embodied Framework for Cross-Embodiment and Multi-Agent Collaboration
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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
Forward citations
Cited by 9 Pith papers
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REAL, a benchmark and trained vision-language agent for oracle-free mobile manipulation with user interaction, achieves 78.3% end-to-end success on 60 physical-robot episodes after simulation-only high-level training.
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Mosaic delivers 27-32% faster multi-agent embodied execution and 4-10 point higher success via agent-centric relative memory plus per-step ILP action allocation.
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AEROS: A Single-Agent Operating Architecture with Embodied Capability Modules
Robots are modeled as single persistent agents extended by installable Embodied Capability Modules under policy-enforced runtime, yielding 100% task success in simulation versus lower baseline rates.
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RoboStream: Weaving Spatio-Temporal Reasoning with Memory in Vision-Language Models for Robotics
Training-free STF-Tokens plus a Causal Spatio-Temporal Graph let VLMs keep object permanence and action history, raising long-horizon robotic manipulation success far above reactive baselines.
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Towards Spatial Trace with Reasoning in Vision-Language Models for Robotics
A 3D-aware VLM, RoboTracer, generates metric-grounded spatial traces for robot manipulation using scale supervision and metric-sensitive reinforcement rewards.
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RoboBrain 2.0 Technical Report
RoboBrain 2.0, a 7B/32B embodied vision-language model built on Qwen2.5-VL, reports state-of-the-art or near-top scores on several spatial and temporal reasoning benchmarks for robotics.
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Mimir: A Neuro-Symbolic Memory System with Dynamic Grounding for Embodied Agents in Interactive Environments
Separating world memory from task memory and grounding each goal in recalled evidence improves embodied-agent success rates by up to 42.5 points across 13 vision-language backbones.
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