A friction-model-based framework for Reinforcement Learning of robotic tasks in non-rigid environments

Adrià Colomé; Antoni Planells; Carme Torras

ICRA 2015poster81 citations

A friction-model-based framework for Reinforcement Learning of robotic tasks in non-rigid environments

Adrià Colomé, Antoni Planells, Carme Torras

Abstract

Learning motion tasks in a real environment with deformable objects requires not only a Reinforcement Learning (RL) algorithm, but also a good motion characterization, a preferably compliant robot controller, and an agent giving feedback for the rewards/costs in the RL algorithm. In this paper, we unify all these parts in a simple but effective way to properly learn safety-critical robotic tasks such as wrapping a scarf around the neck (so far, of a mannequin).

BibTeX

@inproceedings{icra2015_africtionmodelba,
  title = {A friction-model-based framework for Reinforcement Learning of robotic tasks in non-rigid environments},
  author = {Adrià Colomé and Antoni Planells and Carme Torras},
  booktitle = {ICRA 2015},
  year = {2015}
}