Automatic extraction of abstract actions from humanoid motion data

Abstract

Developing a humanoid robot that can learn to perform complex tasks by itself has become a major goal of robotics research. This paper proposes a new algorithm for the automatic segmentation of humanoid motion data. We use a simplified soccer game, RoboCup, as a prototype task. Motion data from a 20 degree-of-freedom humanoid soccer playing robot are reduced to their intrinsic dimensionality by nonlinear principal component analysis. The proposed algorithm operates in of two phases. The first phase automatically segments the motion data in the reduced sensorimotor space by incrementally generating nonlinear principal component analysis with a circular constraint networks and assigning data points based on their temporal order to these networks in a conquer-and-divide fashion. Then, the second phase of the algorithm removes repeated patterns based on the distance between redundant motion patterns in the reduced sensorimotor space. The networks abstracted five motion patterns without any prior information about the number or type of motion patterns. The learned networks can be used to recognize and generate humanoid actions.