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Generalizing Demonstrated Actions in Manipulation Tasks




Programming-by-demonstration promises to significantly reduce the burden of coding robots to perform new tasks. However, service robots will be presented with a variety of different situations that were not specifically demonstrated to it. In such cases, the robot must autonomously generalize its learned motions to these new situations. We propose a system that can generalize movements to new target locations and even new objects. The former is achieved by using a task-specific coordinate system together with dynamical systems motor primitives. Generalizing actions to new objects is a more complex problem, which we solve by treating it as a continuum-armed bandits problem. Using the bandits framework, we can efficiently optimize the learned action for a specific object. The proposed method was implemented on a real robot and succesfully adapted the grasping action to three different objects. Although we focus on grasping as an example of a task, the proposed methods are much more widely applicable to robot manipulation tasks.

Author(s): Kroemer, O. and Detry, R. and Piater, J. and Peters, J.
Journal: IROS 2010 Workshop on Grasp Planning and Task Learning by Imitation
Volume: 2010
Pages: 1
Year: 2010
Month: October
Day: 0

Department(s): Empirical Inference
Bibtex Type: Poster (poster)

Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Generalizing Demonstrated Actions in Manipulation Tasks},
  author = {Kroemer, O. and Detry, R. and Piater, J. and Peters, J.},
  journal = {IROS 2010 Workshop on Grasp Planning and Task Learning by Imitation},
  volume = {2010},
  pages = {1},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = oct,
  year = {2010},
  month_numeric = {10}