基于弹性地图的机器人演示学习
Robot Learning from Demonstration Using Elastic Maps
AI总结：
本文提出一种基于优化的演示学习方法，将演示编码为弹性地图，通过三个具有物理意义的凸优化目标拟合技能模型，并在UR5e机械臂上验证了其优于其他LfD方法的性能与灵活性。
摘要：
演示学习（LfD）是一种从人类提供的演示中复现和泛化机器人技能的常用方法。本文提出了一种新颖的基于优化的LfD方法，将演示编码为弹性地图。弹性地图是由弹簧网格连接的节点图。我们通过将弹性地图拟合到演示集合来构建技能模型。本文方法中构建的优化问题包含三个具有自然和物理解释的目标项。主项奖励笛卡尔坐标中的均方误差。第二项惩罚点的非等距分布，从而得到轨迹的最优总长度。第三项在惩罚非线性的同时奖励平滑性。这些二次目标构成一个凸问题，可以通过局部优化器高效求解。我们考察了九种构建和加权弹性地图的方法，并研究了它们在机器人任务中的性能。我们还在使用UR5e机械臂的多个仿真和真实世界实验中评估了所提出的方法，并将其与其他LfD方法进行比较，以展示其在多种指标上的优势和灵活性。
英文摘要：
Learning from Demonstration (LfD) is a popular method of reproducing and generalizing robot skills from human-provided demonstrations. In this paper, we propose a novel optimization-based LfD method that encodes demonstrations as elastic maps. An elastic map is a graph of nodes connected through a mesh of springs. We build a skill model by fitting an elastic map to the set of demonstrations. The formulated optimization problem in our approach includes three objectives with natural and physical interpretations. The main term rewards the mean squared error in the Cartesian coordinate. The second term penalizes the non-equidistant distribution of points resulting in the optimum total length of the trajectory. The third term rewards smoothness while penalizing nonlinearity. These quadratic objectives form a convex problem that can be solved efficiently with local optimizers. We examine nine methods for constructing and weighting the elastic maps and study their performance in robotic tasks. We also evaluate the proposed method in several simulated and real-world experiments using a UR5e manipulator arm, and compare it to other LfD approaches to demonstrate its benefits and flexibility across a variety of metrics.
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。
