@conference {
	title = {Co-Active Learning to Adapt Humanoid Movement for Manipulation.},
	author = {Mao, Ren., Baras, John S., Yang, Yezhou., Fermuller, Cornelia.},
	year = {2016},
	month = {11/2016},
	abstract = {In this paper, we address the problem of interactive~robot movement adaptation under various environmental~constraints. A common approach is to adopt motion primitives~to generate target motions from demonstrations. However,~their generalization capability is weak for novel environments.
Additionally, traditional motion generation methods do not~consider versatile constraints from different users, tasks, and~environments. In this work, we propose a co-active learning~framework for learning to adapt the movement of robot end~effectors for manipulation tasks. It is designed to adapt the~original imitation trajectories, which are learned from demonstrations to novel situations with different constraints. The~framework also considers user feedback towards the adapted~trajectories, and it learns to adapt movement through human in-the-loop interactions. Experiments on a humanoid platform~validate the e effectiveness of our approach.},
	address = {The Westin Resort \& Spa},
	booktitle = {2016 IEEE-RAS International Conference on Humanoid Robots (Humanoids 2016)},
	pages = {pp. 372-378},
	organization = {Cancun, Mexico.}
}
