@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 = {Active sampling exploiting detector response pattern for efficient target detection},
	author = {Luan, Wentao., Mao, Ren., Baras, John S.},
	year = {2016},
	month = {07/2016},
	abstract = {Detecting targets in an image is a fundamental task in computer vision and robotic system. When only a trained detector (binary classifier) is at hand, the target detection problem becomes localizing the correct windows containing targets in the image and can be considered as a sampling problem. Exhaustive sliding window method is a common approach, but it is usually computationally expensive especially when the detection algorithm is time-consuming. In this work, we observe that detector{\textquoteright}s response scores of sampling windows fade gradually from the peak response window in the detection area and we approximate this scoring pattern with an exponential decay function. By exploiting this property, we propose an active sampling method for efficient target detection to avoid exhaustively searching all the window space. The method estimates the probability of windows containing the target by fusing information from sampled windows and their detector{\textquoteright}s scores and then decides the next window to be observed. Experiments have shown that our proposed method improves efficiency in human detection applications as it requires fewer windows to achieve similar performance compared to sliding windows and multi-stage particle window (MS-PW) method.},
	address = {Heidelberg , Germany},
	booktitle = {19th Intenation Conference on Information Fusion},
	pages = {pp. 1916-1922}
}
