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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.00649 |
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| _version_ | 1866916418076803072 |
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| author | Bhattacharjee, Saswati Sinha, Anirban Ekenna, Chinwe |
| author_facet | Bhattacharjee, Saswati Sinha, Anirban Ekenna, Chinwe |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_00649 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LASMP: Language Aided Subset Sampling Based Motion Planner Bhattacharjee, Saswati Sinha, Anirban Ekenna, Chinwe Robotics Artificial Intelligence Human-Computer Interaction Machine Learning 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. |
| title | LASMP: Language Aided Subset Sampling Based Motion Planner |
| topic | Robotics Artificial Intelligence Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2410.00649 |