@conference {
	title = {Fast Task-Specific Target Detection via Graph Based Constraints Representation and Checking},
	author = {Luan, Wentao., Yang, Yezhou., Fermuller, Cornelia., Baras, John S.},
	year = {2017},
	month = {05/2017},
	abstract = {We present a framework for fast target detection in real-world robotics applications. Considering that an intelligent agent attends to a task-specific object target during execution, our goal is to detect the object efficiently. We propose the concept of early recognition, which influences the candidate proposal process to achieve fast and reliable detection performance. To check the target constraints efficiently, we put forward a novel policy which generates a sub-optimal checking order, and we prove that it has bounded time cost compared to the optimal checking sequence, which is not achievable in polynomial time. Experiments on two different scenarios: 1) rigid object and 2) non-rigid body part detection validate our pipeline. To show that our method is widely applicable, we further present a human-robot interaction system based on our non-rigid body part detection.},
	address = {Singapore},
	booktitle = {2017 IEEE International Conference on Robotics and Automation},
	pages = {pp.  3984-3991},
	organization = {Marina Bay Sands Convection Center}
}
@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}
}
@conference {
	title = {Learning Hand Movements from Markerless Demonstrations for Humanoid Tasks},
	author = {Mao, Ren., Yang, Yezhou., Fermuller, Cornelia., Aloimones, Yiannis., Baras, John S.},
	year = {2014},
	month = {11/2014},
	abstract = {We present a framework for generating trajectories of the hand movement during manipulation actions from demonstrations so the robot can perform similar actions in new situations. Our contribution is threefold: 1) we extract and transform hand movement trajectories using a state-of-the-art markerles full hand model tracker from Kinect sensor data; 2)we develop a new bio-inspired trajectory segmentation method that automatically segments complex movements into action units, and 3) we develop a generative method to learn task specific control using Dynamic Movement Primitives (DMPs).Experiments conducted both on synthetic data and real data using the Baxter research robot platform validate our approach.},
	address = {Madrid, Spain},
	booktitle = {2014 IEEE-RAS International Conference on Humanoid Robots (Humanoids 2014)},
	pages = {pp. 938-943}
}
