机器人运动轨迹的模仿学习综述EI北大核心CSCD

作为机器人技能学习中的一个重要分支,模仿学习近年来在机器人系统中得到了广泛的应用.模仿学习能够将人类的技能以一种相对直接的方式迁移到机器人系统中,其思路是先从少量示教样本中提取相应的运动特征,然后将该特征泛化到新的情形.本文针对机器人运动轨迹的模仿学习进行综述.首先详细解释模仿学习中的技能泛化、收敛性和外插等基本问题;其次从原理上对动态运动基元、概率运动基元和核化运动基元等主要的模仿学习算法进行介绍;然后深入地讨论模仿学习中姿态和刚度矩阵的学习问题、协同和不确定性预测的问题以及人机交互中的模仿学习等若干关键问题;最后本文探讨了结合因果推理的模仿学习等几个未来的发展方向.

On Imitation Learning of Robot Movement Trajectories:A Survey

As a promising direction in the community of robot learning,imitation learning has achieved great success in a myriad of robotic systems.Imitation learning is capable of providing a straightforward way to transfer human skills to robots by extracting motion features from few demonstrations and subsequently employing them to new scenarios.This paper will review literature on trajectory learning by imitation for robots.The basic problems in imitation learning are first described in detail,such as skill adaptation,convergence and extrapolation.After that,state-of-the-art approaches are introduced,including dynamical movement primitives,probabilistic movement primitives and kernelized movement primitives.Later,various key problems are explained at length,e.g.,learning of orientations and stiffness matrices,synergy and uncertainty prediction,as well as imitation learning in human-robot interaction.Finally,the possible future directions of imitation learning,for instance,the combination of imitation learning and causal inference,are discussed.