Learning visually guided grasping: a test case in sensorimotor learning

Abstract

We present a general scheme for learning sensorimotor tasks, which allows rapid online learning and generalization of the learned knowledge to unfamiliar objects. The scheme consists of two modules, the first generating candidate actions and the second estimating their quality. Both modules work in an alternating fashion until an action which is expected to provide satisfactory performance is generated, at which point the system executes the action. We developed a method for off-line selection of heuristic strategies and quality predicting features, based on statistical analysis. The usefulness of the scheme was demonstrated in the context of learning visually guided grasping. We consider a system that coordinates a parallel-jaw gripper and a fixed camera. The system learns to estimate grasp quality by learning a function from relevant visual features to the quality. An experimental setup using an AdeptOne manipulator was developed to test the scheme.