Classification of Motion Constraints by Explorative Manipulation by a Compliant Multi-Fingered Hand

Abstract

We propose acquiring several properties of an unknown manipulated object, through without using arbitrary information. It consists of explorative manipulation and observation with sensors. By observing self-motion with the target object, it acquires time series sensor data embedded in the motion constraints of the manipulated object. We assume that manipulation features are expressed as a cooperative relation between the fingers and the relation is extractable as a correlation of the time series sensor data. High-order local autocorrelation widely used in image recognition provides the feature vector from data. In feature space, contrastive motion constraints construct the axis of variance. Principal component analysis (PCA) finds the axis mapping constraints. Clustering is used to make classes corresponding to constraints in PCA space. The classes correspond to symbolic representation for the robot. The efficacy of our proposal is demonstrated through simulation and experiments in a task involving opening a screw on lid of unknown size from a bottle.