In this paper we present a new robot control and learning system that allows a humanoid robot to extend its movement repertoire by learning from a human tutor. The focus is learning and imitating motor skills to move and position objects. We concentrate on two major aspects. First, the presented teaching and imitation scenario is fully interactive. A human tutor can teach the robot which is in turn able to integrate newly learned skills into different movement sequences online. Second, we combine a number of novel concepts to enhance the flexibility and generalization capabilities of the system. Generalization to new tasks is obtained by decoupling the learned movements from the robot’s embodiment using a task space representation. It is chosen automatically from a commonly used task space pool. The movement descriptions are further decoupled from specific object instances by formulating them with respect to so-called linked objects. They act as references and can interactively be bound to real objects. When executing a learned task, a flexible kinematic description allows to change the robot’s body schema online and thereby apply the learned movement relative to different body parts or new objects. An efficient optimization scheme adapts movements to such situations performing online obstacle and self-collision avoidance. Finally, all described processes are combined within a comprehensive architecture. To demonstrate the generalization capabilities we show experiments where the robot performs a movement bimanually in different environments, although the task was demonstrated by the tutor only one-handed.
展开▼
