Thinking and Moving: An Efficient Computing Approach for Integrated Task and Motion Planning in Cooperative Embodied AI Systems

Cooperative embodied AI systems, where multiple agents collaborate to accomplish complex, long-horizon tasks, show significant promise for real-world applications. These systems integrate perception, cognition, and action through integrated task and motion planning (TAMP), leveraging the advanced reasoning and communication capabilities of large language models (LLMs). However, their efficiency is often hindered by challenges such as high computational latency and redundant communication, largely due to the reliance on LLMs for sequential planning decisions.