Human sensorimotor learning for humanoid robot skill synthesis

Abstract

Humans are very skilled at learning new control tasks, and in particular, the use of novel tools. In this article we propose a paradigm that utilizes this sensorimotor learning capacity to obtain robot behaviors, which would otherwise require manual programming by experts. The concept is to consider the target robot platform as a tool to be controlled intuitively by a human. The human is therefore provided with an interface designed to make the control of the robot intuitive, and learns to perform a given task using the robot. This is akin to the stage where a beginner learns to drive a car. After human learning, the skilled control of the robot is used to build an autonomous controller so that the robot can perform the task without human guidance. We demonstrate the feasibility of this proposal for humanoid robot skill synthesis by showing how a statically stable reaching skill can be obtained by means of this framework. In addition, we analyze the feedback interface component of this paradigm by examining a dynamics task, in which a human learns to use the motion of the body to control the posture of an inverted pendulum that approximates a humanoid robot, so that it stays upright.