Experience-based imitation using RNNPB

Abstract— Robot imitation is a useful and promising alternative to robot programming. Robot imitation involves two crucial issues. The first is how a robot can imitate a human whose physical structure and properties differ greatly from its own. The second is how the robot can generate various motions from finite programmable patterns (generalization). This paper describes a novel approach to robot imitation based on its own physical experiences. Let us consider a target task of moving an object on a table. For imitation, we focused on an active sensing process in which the robot acquires the relation between the object’s motion and its own arm motion. For generalization, we applied a recurrent neural network with parametric bias (RNNPB) model to enable recognition/generation of imitation motions. The robot associates the arm motion which reproduces the observed object’s motion presented by a human operator. Experimental results demonstrated that our method enabled the robot to ...

• Type: Journal
• Year: 2007
• Where: AR
• Authors: Ryunosuke Yokoya, Tetsuya Ogata, Jun Tani, Kazunori Komatani, Hiroshi G. Okuno
• Keywords: AR 2007, Arm Motion, Robot, Robot Imitation