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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2411.17293 |
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| _version_ | 1866916496487219200 |
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| author | Dang, Xuzhe Edelkamp, Stefan |
| author_facet | Dang, Xuzhe Edelkamp, Stefan |
| contents | Efficiently finding safe and feasible trajectories for mobile objects is a critical field in robotics and computer science. In this paper, we propose SIL-RRT*, a novel learning-based motion planning algorithm that extends the RRT* algorithm by using a deep neural network to predict a distribution for sampling at each iteration. We evaluate SIL-RRT* on various 2D and 3D environments and establish that it can efficiently solve high-dimensional motion planning problems with fewer samples than traditional sampling-based algorithms. Moreover, SIL-RRT* is able to scale to more complex environments, making it a promising approach for solving challenging robotic motion planning problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17293 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SIL-RRT*: Learning Sampling Distribution through Self Imitation Learning Dang, Xuzhe Edelkamp, Stefan Robotics Efficiently finding safe and feasible trajectories for mobile objects is a critical field in robotics and computer science. In this paper, we propose SIL-RRT*, a novel learning-based motion planning algorithm that extends the RRT* algorithm by using a deep neural network to predict a distribution for sampling at each iteration. We evaluate SIL-RRT* on various 2D and 3D environments and establish that it can efficiently solve high-dimensional motion planning problems with fewer samples than traditional sampling-based algorithms. Moreover, SIL-RRT* is able to scale to more complex environments, making it a promising approach for solving challenging robotic motion planning problems. |
| title | SIL-RRT*: Learning Sampling Distribution through Self Imitation Learning |
| topic | Robotics |
| url | https://arxiv.org/abs/2411.17293 |