Path Planning using Instruction-Guided Probabilistic Roadmaps
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915167725420544 |
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| author | Bao, Jiaqi Yonetani, Ryo |
| author_facet | Bao, Jiaqi Yonetani, Ryo |
| contents | This work presents a novel data-driven path planning algorithm named Instruction-Guided Probabilistic Roadmap (IG-PRM). Despite the recent development and widespread use of mobile robot navigation, the safe and effective travels of mobile robots still require significant engineering effort to take into account the constraints of robots and their tasks. With IG-PRM, we aim to address this problem by allowing robot operators to specify such constraints through natural language instructions, such as ``aim for wider paths'' or ``mind small gaps''. The key idea is to convert such instructions into embedding vectors using large-language models (LLMs) and use the vectors as a condition to predict instruction-guided cost maps from occupancy maps. By constructing a roadmap based on the predicted costs, we can find instruction-guided paths via the standard shortest path search. Experimental results demonstrate the effectiveness of our approach on both synthetic and real-world indoor navigation environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16515 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Path Planning using Instruction-Guided Probabilistic Roadmaps Bao, Jiaqi Yonetani, Ryo Robotics This work presents a novel data-driven path planning algorithm named Instruction-Guided Probabilistic Roadmap (IG-PRM). Despite the recent development and widespread use of mobile robot navigation, the safe and effective travels of mobile robots still require significant engineering effort to take into account the constraints of robots and their tasks. With IG-PRM, we aim to address this problem by allowing robot operators to specify such constraints through natural language instructions, such as ``aim for wider paths'' or ``mind small gaps''. The key idea is to convert such instructions into embedding vectors using large-language models (LLMs) and use the vectors as a condition to predict instruction-guided cost maps from occupancy maps. By constructing a roadmap based on the predicted costs, we can find instruction-guided paths via the standard shortest path search. Experimental results demonstrate the effectiveness of our approach on both synthetic and real-world indoor navigation environments. |
| title | Path Planning using Instruction-Guided Probabilistic Roadmaps |
| topic | Robotics |
| url | https://arxiv.org/abs/2502.16515 |