Learning Stylistic Dynamic Movement Primitives from multiple demonstrations

In this paper, we propose a novel concept of movement primitives called Stylistic Dynamic Movement Primitives (SDMPs) for motor learning and control in humanoid robotics. In the SDMPs, a diversity of styles in human motion observed through multiple demonstrations can be compactly encoded in a movement primitive, and this allows style manipulation of motion sequences generated from the movement primitive by a control variable called a style parameter. Focusing on discrete movements, a model of the SDMPs is presented as an extension of Dynamic Movement Primitives (DMPs) proposed by Ijspeert et al.. A novel learning procedure of the SDMPs from multiple demonstrations, including a diversity of motion styles, is also described. We present two practical applications of the SDMPs, i.e., stylistic table tennis swings and obstacle avoidance with an anthropomorphic manipulator.