LASMP: Language Aided Subset Sampling Based Motion Planner

This paper presents the Language Aided Subset Sampling Based Motion Planner (LASMP), a framework that helps mobile robots plan their movements from natural language instructions. LASMP uses a modified version of the Rapidly Exploring Random Tree (RRT) method, which is guided by user-provided instructions processed through a language model. The planner improves efficiency by focusing on specific areas of the robot’s workspace based on these instructions, making it faster and less resource-intensive. Compared to traditional RRT and RRT* methods, LASMP reduces the number of nodes needed by 55% and cuts random sample queries by 80%, while still generating safe, collision-free paths. Tested in both simulated and real-world environments, LASMP has shown better performance in handling complex indoor scenarios. The results highlight the potential of combining language processing with motion planning to make robot navigation more efficient.