Two papers from CPSLAB are accepted to ICRA 2016


Following two papers are accepted to the IEEE International Conference on Robotics and Automation (ICRA 2016):

  • Robust Learning from Demonstration Using Leveraged Gaussian Processes and Sparse-Constrained Optimization by Sungjoon Choi, Kyungjae Lee, and Songhwai Oh
    • Abstract: In this paper, we propose a novel method for robust learning from demonstration using leveraged Gaussian process regression. While existing learning from demonstration (LfD) algorithms assume that demonstrations are given from skillful experts, the proposed method alleviates such assumption by allowing demonstrations from casual or novice users. To learn from demonstrations of mixed quality, we present a sparse-constrained leveraged optimization algorithm using proximal linearized minimization. The proposed sparse constrained leverage optimization algorithm is successfully applied to sensory field reconstruction and direct policy learning for planar navigation problems. In experiments, the proposed sparse-constrained method outperforms existing LfD methods.
  • Multiple-Hypothesis Chance-Constrained Target Tracking Under Identity Uncertainty by Yoonseon Oh and Songhwai Oh
    • Abstract: We propose a robust target tracking algorithm for a mobile robot under identity uncertainty, which arises in crowded environments. When a mobile robot has a sensor with a fan-shaped field of view and finite sensing region, the proposed algorithm aims to minimize the probability of losing a moving target. We predict the next position of a moving target in a crowded environment using a multiple-hypothesis prediction algorithm which combines the motion model and appearance model of the target. When the distribution of the target’s next position follows a Gaussian mixture model, the proposed tracking algorithm can track a target with a guaranteed tracking success probability. If the tracking success probability is sufficiently good, the method minimizes the moving distance of the mobile robot. The performance of the method is extensively validated in simulation and experiments using a Pioneer robot with a Microsoft Kinect sensor.
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