Reinforcement Learning for Adaptive Planner Parameter Tuning: A Perspective on Hierarchical Architecture
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866913754148503552 |
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| author | Wangtao, Lu Yufei, Wei Jiadong, Xu Wenhao, Jia Liang, Li Rong, Xiong Yue, Wang |
| author_facet | Wangtao, Lu Yufei, Wei Jiadong, Xu Wenhao, Jia Liang, Li Rong, Xiong Yue, Wang |
| contents | Automatic parameter tuning methods for planning algorithms, which integrate pipeline approaches with learning-based techniques, are regarded as promising due to their stability and capability to handle highly constrained environments. While existing parameter tuning methods have demonstrated considerable success, further performance improvements require a more structured approach. In this paper, we propose a hierarchical architecture for reinforcement learning-based parameter tuning. The architecture introduces a hierarchical structure with low-frequency parameter tuning, mid-frequency planning, and high-frequency control, enabling concurrent enhancement of both upper-layer parameter tuning and lower-layer control through iterative training. Experimental evaluations in both simulated and real-world environments show that our method surpasses existing parameter tuning approaches. Furthermore, our approach achieves first place in the Benchmark for Autonomous Robot Navigation (BARN) Challenge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_18366 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning for Adaptive Planner Parameter Tuning: A Perspective on Hierarchical Architecture Wangtao, Lu Yufei, Wei Jiadong, Xu Wenhao, Jia Liang, Li Rong, Xiong Yue, Wang Robotics Automatic parameter tuning methods for planning algorithms, which integrate pipeline approaches with learning-based techniques, are regarded as promising due to their stability and capability to handle highly constrained environments. While existing parameter tuning methods have demonstrated considerable success, further performance improvements require a more structured approach. In this paper, we propose a hierarchical architecture for reinforcement learning-based parameter tuning. The architecture introduces a hierarchical structure with low-frequency parameter tuning, mid-frequency planning, and high-frequency control, enabling concurrent enhancement of both upper-layer parameter tuning and lower-layer control through iterative training. Experimental evaluations in both simulated and real-world environments show that our method surpasses existing parameter tuning approaches. Furthermore, our approach achieves first place in the Benchmark for Autonomous Robot Navigation (BARN) Challenge. |
| title | Reinforcement Learning for Adaptive Planner Parameter Tuning: A Perspective on Hierarchical Architecture |
| topic | Robotics |
| url | https://arxiv.org/abs/2503.18366 |