受可微逻辑规范约束的规划与控制策略协同学习
Co-learning Planning and Control Policies Constrained by Differentiable Logic Specifications
AI总结：
本文提出协同学习规划与控制策略的强化学习方法，以可微逻辑规范约束高维机器人导航，降低样本复杂度并实现长时域路径生成与策略对齐。
摘要：
在机器人学中，规划与控制策略的综合是一项基础任务，而复杂逻辑规范和高维机器人动力学等因素使其进一步复杂化。本文提出一种新颖的强化学习方法，通过协同学习规划与控制策略，求解具有复杂逻辑规范的高维机器人导航任务。值得注意的是，该方法显著降低了训练中的样本复杂度，使我们能够以远少于现有强化学习算法的样本训练高质量策略。此外，我们的方法简化了从地图图像中提取复杂规范的流程，并能够在不同地图布局上高效生成长时域机器人运动路径。而且，我们的方法还通过策略对齐展现出高维控制和避免次优策略的能力。通过涉及仿真高维四足机器人动力学以及真实世界差速驱动机器人（TurtleBot3）在不同类型任务规范下的实验，证明了本方法的有效性。
英文摘要：
Synthesizing planning and control policies in robotics is a fundamental task, further complicated by factors such as complex logic specifications and high-dimensional robot dynamics. This paper presents a novel reinforcement learning approach to solving high-dimensional robot navigation tasks with complex logic specifications by co-learning planning and control policies. Notably, this approach significantly reduces the sample complexity in training, allowing us to train high-quality policies with much fewer samples compared to existing reinforcement learning algorithms. In addition, our methodology streamlines complex specification extraction from map images and enables the efficient generation of long-horizon robot motion paths across different map layouts. Moreover, our approach also demonstrates capabilities for high-dimensional control and avoiding suboptimal policies via policy alignment. The efficacy of our approach is demonstrated through experiments involving simulated high-dimensional quadruped robot dynamics and a real-world differential drive robot (TurtleBot3) under different types of task specifications.
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。
