Imitation learning with generalized task descriptions

14 years 1 months ago
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...
Clemens Eppner, Jürgen Sturm, Maren Bennewitz
Added 23 May 2010
Updated 23 May 2010
Type Conference
Year 2009
Where ICRA
Authors Clemens Eppner, Jürgen Sturm, Maren Bennewitz, Cyrill Stachniss, Wolfram Burgard
Comments (0)