LASMP: Language Aided Subset Sampling Based Motion Planner
<|reference_start|>LASMP: Language Aided Subset Sampling Based Motion Planner: This paper presents the Language Aided Subset Sampling Based Motion Planner (LASMP), a system that helps mobile robots plan their movements by using natural language instructions. LASMP uses a modified version of the Rapidly Exploring Random Tree (RRT) method, which is guided by user-provided commands processed through a language model (RoBERTa). The system 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 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.<|reference_end|>
@article{bhattacharjee2024lasmp:,
    title={LASMP: Language Aided Subset Sampling Based Motion Planner},
    author={Saswati Bhattacharjee, Anirban Sinha, Chinwe Ekenna},
    journal={arXiv preprint arXiv:2410.00649},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.00649},
    primaryClass={cs.RO cs.AI cs.HC cs.LG}
}