Online acquisition and visualization of motion primitives for humanoid robots

This paper proposes an on-line, interactive approach for incremental learning and visualization of full body motion primitives from observation of human motion. The human demonstrator motion is captured in a motion capture studio. The continuous observation sequence is first partitioned into motion segments, using stochastic segmentation. Motion segments are next incrementally clustered and organized into a hierarchical tree structure representing the known motion primitives. At the same time, the sequential relationship between motion primitives is learned, to enable the generation of coherent sequences of motion primitives. An on-line visualization system is also developed to allow the demonstrator to visualize the motion database and the motion primitives learned by the system, thus giving the demonstrator insight into the learning process and the ability to interactively modify the demonstration based on the current state of the knowledge base. The developed system has many potential applications for motion analysis, prediction and imitation learning for humanoid robots.