Imitation learning with generalized task descriptions

— In this paper, we present an approach that allows a robot to observe, generalize, and reproduce tasks observed from multiple demonstrations. Motion capture data is recorded in which a human instructor manipulates a set of objects. In our approach, we learn relations between body parts of the demonstrator and objects in the scene. These relations result in a generalized task description. The problem of learning and reproducing human actions is formulated using a dynamic Bayesian network (DBN). The posteriors corresponding to the nodes of the DBN are estimated by observing objects in the scene and body parts of the demonstrator. To reproduce a task, we seek for the maximum-likelihood action sequence according to the DBN. We additionally show how further constraints can be incorporated online, for example, to robustly deal with unforeseen obstacles. Experiments carried out with a real 6-DoF robotic manipulator as well as in simulation show that our approach enables a robot to reproduc...

• Type: Conference
• Year: 2009
• Where: ICRA
• Authors: Clemens Eppner, Jürgen Sturm, Maren Bennewitz, Cyrill Stachniss, Wolfram Burgard
• Keywords: Body Parts, Generalized Task Description, ICRA 2009, Motion Capture Data, Robotics