Developing Robot Reaching Skill with Relative-Location based Approximating

Abstract

Robot reaching is a fundamental skill for knowing about the environment through interacting with objects and completing complex manipulation tasks. The topic has been studied widely for decades. In the paper, with reference to the relevant mechanism of human, a novel strategy for developing robot reaching skill is proposed, in which the whole process is divided into two stages including rough reaching and iterative adjustment. Generally in the process of obtaining spatial information of target object, the accuracy of the absolute positioning might be severely affected due to inevitable errors derived from sensing means (e.g. camera) in real world scenario. On the contrary, the accuracy of relative positioning will be much better, in which we only require answering the relative location between the target and the end-effector. Under this view, the proposed method, called the relative-location based approximating strategy (RLA), firstly attempts to move the end-effector to the target roughly with a simple inverse model, and then gradually approximates to the target according to the information of the relative location, i.e. the direction of the target relative to the end-effector. To accomplish such an approximating process, an internal model regarding to base directions is developed, where the motor babbling is involved under the inspiration of infants development mechanism. The approach was experimentally validated using the child-sized physical humanoid robot PKU-HR6.0II in a completely autonomous style and the results illustrate the effectiveness and superiority of the proposed strategy.