ROSClaw: A Hierarchical Semantic-Physical Framework for Heterogeneous Multi-Agent Collaboration

Researchers introduce ROSClaw, a new AI framework that integrates large language models with robotic systems to improve multi-agent collaboration and long-horizon task execution. The framework addresses critical gaps between semantic understanding and physical execution by using unified vision-language models and enabling real-time coordination between simulated and real-world robots.

• →ROSClaw framework integrates LLMs with embodied agents to bridge the gap between semantic understanding and physical robot execution.
• →The system uses e-URDF representations and sim-to-real topological mapping for real-time access to physical states of heterogeneous robots.
• →Framework includes automated data collection and iterative policy optimization during real-world execution.
• →Unified agent controller maintains semantic continuity and dynamically assigns tasks to different robots for improved multi-policy execution.
• →System supports cross-platform transfer and automated SDK-level control program generation to minimize robot-specific development workflows.