弥合差距：结合安全模块的改进型经典运动规划的正则化强化学习
Bridging the Gap: Regularized Reinforcement Learning for Improved Classical Motion Planning with Safety Modules
AI总结：
提出利用经典算法指导强化学习并引入安全后备系统的方法，在无需人类示范的情况下提升了RL的收敛速度与结果，实现了样本高效且保证安全的移动导航。
摘要：
经典导航规划器能提供安全的导航，但往往次优且难以符合人类规范。基于机器学习的现代自主导航算法能模仿更自然且符合人类规范的导航，但通常需要庞大且真实的数据集，且并不总能提供安全保证。我们提出了一种利用经典算法来指导强化学习的方法。这极大地改善了底层RL算法的结果和收敛速度，且无需人类专家示范来启动该过程。此外，我们引入了一个实用的后备系统，可切换回经典规划器以确保安全。最终成果是一种用于移动导航的样本高效的机器学习方法，它建立在经典算法之上，对其进行改进以确保符合人类规范，并保证安全性。
英文摘要：
Classical navigation planners can provide safe navigation, albeit often suboptimally and with hindered human norm compliance. ML-based, contemporary autonomous navigation algorithms can imitate more natural and humancompliant navigation, but usually require large and realistic datasets and do not always provide safety guarantees. We present an approach that leverages a classical algorithm to guide reinforcement learning. This greatly improves the results and convergence rate of the underlying RL algorithm and requires no human-expert demonstrations to jump-start the process. Additionally, we incorporate a practical fallback system that can switch back to a classical planner to ensure safety. The outcome is a sample efficient ML approach for mobile navigation that builds on classical algorithms, improves them to ensure human compliance, and guarantees safety.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
