用分层强化学习自动调参，提升机器人导航的稳定性与效率。
Reinforcement Learning for Adaptive Planner Parameter Tuning: A Perspective on Hierarchical Architecture
- 分三层结构：低频调参、中频规划、高频控制，协同优化
- 在仿真和真实环境均超越现有方法，获BARN挑战赛第一名
- 适合需要自适应参数调整的复杂自主系统研发
针对规划算法的自动参数调优方法，结合流水线流程与基于学习的技术，因其稳定性和在高度约束环境中的能力而被视为前景广阔。尽管现有方法已取得显著成效，但进一步性能提升需更系统的架构设计。本文提出一种基于强化学习的分层参数调优架构，包含低频参数调优、中频规划与高频控制三层结构，通过迭代训练实现上层参数调优与下层控制的同步改进。在仿真与真实环境中的实验评估表明，该方法优于现有调参技术，并在自主机器人导航基准测试（BARN Challenge）中夺得第一名。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。