Towards Robust Skill Generalization: Unifying Learning from Demonstration and Motion Planning
Abstract
In this paper, we present Combined Learning from demonstration And Motion Planning (CLAMP) as an efficient approach to skill learning and generalizable skill reproduction. CLAMP combines the strengths of Learning from Demonstration (LfD) and motion planning into a unifying framework. We carry out probabilistic inference to find trajectories which are optimal with respect to a given skill and also feasible in different scenarios. We use factor graph optimization to speed up inference. To encode optimality, we provide a new probabilistic skill model based on a stochastic dynamical system. This skill model requires minimal parameter tuning to learn, is suitable to encode skill constraints, and allows efficient inference. Preliminary experimental results showing skill generalization over initial robot state and unforeseen obstacles are presented.
Cite
Text
Rana et al. "Towards Robust Skill Generalization: Unifying Learning from Demonstration and Motion Planning." Proceedings of the 1st Annual Conference on Robot Learning, 2017.Markdown
[Rana et al. "Towards Robust Skill Generalization: Unifying Learning from Demonstration and Motion Planning." Proceedings of the 1st Annual Conference on Robot Learning, 2017.](https://mlanthology.org/corl/2017/rana2017corl-robust/)BibTeX
@inproceedings{rana2017corl-robust,
title = {{Towards Robust Skill Generalization: Unifying Learning from Demonstration and Motion Planning}},
author = {Rana, Muhammad Asif and Mukadam, Mustafa and Ahmadzadeh, Seyed Reza and Chernova, Sonia and Boots, Byron},
booktitle = {Proceedings of the 1st Annual Conference on Robot Learning},
year = {2017},
pages = {109-118},
volume = {78},
url = {https://mlanthology.org/corl/2017/rana2017corl-robust/}
}