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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2411.15651 |
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| _version_ | 1866917847430594560 |
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| author | Lathrop, John Rivi`ere, Benjamin Alindogan, Jedidiah Chung, Soon-Jo |
| author_facet | Lathrop, John Rivi`ere, Benjamin Alindogan, Jedidiah Chung, Soon-Jo |
| contents | We present Model Predictive Trees (MPT), a receding horizon tree search algorithm that improves its performance by reusing information efficiently. Whereas existing solvers reuse only the highest-quality trajectory from the previous iteration as a "hotstart", our method reuses the entire optimal subtree, enabling the search to be simultaneously guided away from the low-quality areas and towards the high-quality areas. We characterize the restrictions on tree reuse by analyzing the induced tracking error under time-varying dynamics, revealing a tradeoff between the search depth and the timescale of the changing dynamics. In numerical studies, our algorithm outperforms state-of-the-art sampling-based cross-entropy methods with hotstarting. We demonstrate our planner on an autonomous vehicle testbed performing a nonprehensile manipulation task: pushing a target object through an obstacle field. Code associated with this work will be made available at https://github.com/jplathrop/mpt. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_15651 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Model Predictive Trees: Sample-Efficient Receding Horizon Planning with Reusable Tree Search Lathrop, John Rivi`ere, Benjamin Alindogan, Jedidiah Chung, Soon-Jo Robotics Systems and Control We present Model Predictive Trees (MPT), a receding horizon tree search algorithm that improves its performance by reusing information efficiently. Whereas existing solvers reuse only the highest-quality trajectory from the previous iteration as a "hotstart", our method reuses the entire optimal subtree, enabling the search to be simultaneously guided away from the low-quality areas and towards the high-quality areas. We characterize the restrictions on tree reuse by analyzing the induced tracking error under time-varying dynamics, revealing a tradeoff between the search depth and the timescale of the changing dynamics. In numerical studies, our algorithm outperforms state-of-the-art sampling-based cross-entropy methods with hotstarting. We demonstrate our planner on an autonomous vehicle testbed performing a nonprehensile manipulation task: pushing a target object through an obstacle field. Code associated with this work will be made available at https://github.com/jplathrop/mpt. |
| title | Model Predictive Trees: Sample-Efficient Receding Horizon Planning with Reusable Tree Search |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2411.15651 |