Representation of robot motion control skill

Abstract

Development of skilled robotics draws clues from model based theories of human motor control. Thus, a comprehensive anthropomorphic background is given. Skills in robotics are viewed as a tool for fast and efficient real time control that can handle complexity and nonlinearity of robots, generally aiming at robot autonomy. In particular, a skill of redundancy resolution is addressed through a skill representation problem based on Function Approximator. The task of the robot is approximated by a set of parameterized motion primitives. Adopted parameters are also parameters of the function approximator, i.e., skill used. Redundancy is resolved during skill learning based on available expert knowledge, yielding parameterized joint motions. The approximation procedure (Successive Approximations), a major contribution of the paper, is used for batch compilation of parameterized examples, resulting in a parameterized skill model. Such skill enables a user, inexpert in redundancy resolution, to gain benefits from redundant robots. All properties of the Successive Approximations procedure such as accuracy in interpolation and extrapolation, acceleration in redundancy resolution and upgrading to new skill regarding the task variation, are discussed in the example of a five degrees-of-freedom planar redundant robot, performing parameterized ellipse as motion primitive.