複数幾何学を用いた操作スキルの模倣学習
Imitation of Manipulation Skills Using Multiple Geometries
複数の座標系候補から最適な表現を抽出し、リーマン多様体上のガウス分布とiLQRで操作動作を学習・再現する手法を提案し、把持や箱開けタスクで実機検証した。
著者: Boyang Ti, Yongsheng Gao, Jie Zhao, Sylvain Calinon
分類: cs.RO, stat.ML
原文アブストラクト
Daily manipulation tasks are characterized by geometric primitives related to actions and object shapes. Such geometric descriptors are poorly represented by only using Cartesian coordinate systems. In this paper, we propose a learning approach to extract the optimal representation from a dictionary of coordinate systems to encode an observed movement/behavior. This is achieved by using an extension of Gaussian distributions on Riemannian manifolds, which is used to analyse a set of user demonstrations statistically, by considering multiple geometries as candidate representations of the task. We formulate the reproduction problem as a general optimal control problem based on an iterative linear quadratic regulator (iLQR), where the Gaussian distribution in the extracted coordinate systems are used to define the cost function. We apply our approach to object grasping and box opening tasks in simulation and on a 7-axis Franka Emika robot. The results show that the robot can exploit several geometries to execute the manipulation task and generalize it to new situations, by maintaining the invariant characteristics of the task in the coordinate system(s) of interest.
