论文标题
通过Lyapunov功能学习生成稳定和无碰撞政策
Generating Stable and Collision-Free Policies through Lyapunov Function Learning
论文作者
论文摘要
快速可靠的机器人部署的需求正在上升。模仿学习（IL）因制定一系列示范制定运动计划政策而变得流行。但是，IL中的许多方法不能保证产生稳定的政策。生成的策略可能不会收敛到机器人目标，从而降低可靠性，并可能与环境相撞，从而降低系统的安全性。稳定的动态系统（SED）估计器通过限制学习过程中Lyapunov稳定性标准产生稳定的策略，但是必须手动选择Lyapunov候选功能。在这项工作中，我们提出了一种使用单个神经网络模型学习Lyapunov功能和策略的新方法。该方法可以配备凸面对象对的障碍物避免模块，以保证不碰撞。我们证明了我们的方法能够在几个模拟环境中找到策略并转移到现实世界中的情况。
The need for rapid and reliable robot deployment is on the rise. Imitation Learning (IL) has become popular for producing motion planning policies from a set of demonstrations. However, many methods in IL are not guaranteed to produce stable policies. The generated policy may not converge to the robot target, reducing reliability, and may collide with its environment, reducing the safety of the system. Stable Estimator of Dynamic Systems (SEDS) produces stable policies by constraining the Lyapunov stability criteria during learning, but the Lyapunov candidate function had to be manually selected. In this work, we propose a novel method for learning a Lyapunov function and a policy using a single neural network model. The method can be equipped with an obstacle avoidance module for convex object pairs to guarantee no collisions. We demonstrated our method is capable of finding policies in several simulation environments and transfer to a real-world scenario.
