This paper presents a novel method for transferring motion planning and control policies between a teacher and a learner robot. With this work, we propose to reduce the sim-to-real gap, transfer knowledge designed for a specific system into a different robot, and compensate for system aging and failures. To solve this problem we introduce a Schwarz-Christoffel mapping-based method to geometrically stretch and fit the control inputs from the teacher into the learner command space. We also propose a method based on primitive motion generation to create motion plans and control inputs compatible with the learner's capabilities. Our approach is validated with simulations and experiments with different robotic systems navigating occluding environments.
翻译:本文介绍了在教师和学习者机器人之间转移运动规划和控制政策的新颖方法。 通过这项工作,我们建议减少模拟到现实的差距,将特定系统设计的知识转移到不同的机器人,并弥补系统老化和故障。为了解决这个问题,我们引入了基于Schwarz-Christoffel绘图的方法,以几何方式拉伸,并将教师的控件输入匹配到学习者指挥空间。我们还提出了一个基于原始运动生成的方法,以创建与学习者能力相容的运动计划和控制输入。我们的方法通过不同机器人系统的模拟和实验来验证,这些机器人系统正在探索渗透环境。