@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.}
}
@conference {
	title = {Reliable Attribute-Based Object Recognition Using High Predictive Value Classifiers},
	author = {Luan, Wentao., Yang, Yezhou., Fermuller, Cornelia., Baras, John S.},
	year = {2016},
	month = {10/2016},
	abstract = {We consider the problem of object recognition in 3D using an ensemble of attribute-based classifiers. We propose two new concepts to improve classification in practical situations, and show their implementation in an approach implemented for recognition from point-cloud data. First, the viewing conditions can have a strong influence on classification performance. We study the impact of the distance between the camera and the object and propose an approach to fusing multiple attribute classifiers, which incorporates distance into the decision making. Second, lack of representative training samples often makes it difficult to learn the optimal threshold value for best positive and negative detection rate. We address this issue, by setting in our attribute classifiers instead of just one threshold value, two threshold values to distinguish a positive, a negative and an uncertainty class, and we prove the theoretical correctness of this approach. Empirical studies demonstrate the effectiveness and feasibility of the proposed concepts.},
	address = {Amsterdam, Netherlands},
	booktitle = {4th European Conference on Computer Vision},
	pages = {pp. 801-815}
}
