A Dual-Agent Collaboration Framework Based on LLMs for Nursing Robots to Perform Bimanual Coordination Tasks

Zhendong Zhao, Xiaotian Yue, Jiexin Xie, Chuanhong Fang, Zhenzhou Shao, Shijie Guo

Abstract: Dual-arm coordination is a fundamental problem in humanoid nursing robot. Large language model (LLM)-driven dual-arm collaboration is gradually becoming a research hotspot in this field. However, the single-thread LLM task planner lacks the ability of co-scheduling, which leads to poor efficiency in nursing robot. To cope with the problem, this letter proposed a multi-agent LLM solution for the task planning of nursing robot, named DABICO. The framework constructs dual agent systems (left-arm and right-arm) at the levels of communication and decision-making, as well as ensuring a single robot entity. Moreover, we construct corresponding communication mechanism and dialogue protocol to promote the information exchange between the two agents. Finally, validation feedback system is proposed to ensure that the sub-task of each robot arm can be executed successfully. A large set of experiments show that, compared to the single-thread LLM task planner, the DABICO framework is more advantageous when dealing with the bimanual coordination tasks. DABICO is able of accomplishes reasoning rapidly, reducing Replan metrics by $\mathbf{90\%}$ on average, and the improvement with respect to Success rate is $\mathbf{11\%}$ on average. Finally we demonstrate DABICO in real-world medicine organization experiment on a dual-arm nursing robot.

External IDs:dblp:journals/ral/ZhaoYXFSG25