Generalization of orientational motion in unit quaternion space

Abstract

A requirement for the humanoid robot's operation in natural environments is that a humanoid robot can effectively adapt to new configurations of the external world. In this paper we address the problem of adaptation, where new robot movements are generated based on data accumulated in related but different situations. Our solution to this challenge is to apply statistical learning, which provides a method to generate robot responses in new situations. Building on our previous work on learning motor primitives [1], we propose a new methodology for task-specific generalization of orientation trajectories, which we encode as Cartesian space Dynamic Movement Primitives. Example trajectories are generalized by applying Locally Weighted Regression in unit quaternion space, using the parameters describing the task as query points into the trajectory database. We show on real-world and simulated tasks that the proposed methodology can be used for statistical learning of orientation trajectories.