Three papers from CPSLAB are accepted to ICRA 2015


Following three papers are accepted to the IEEE International Conference on Robotics and Automation (ICRA 2015):

  • Leveraged Non-Stationary Gaussian Process Regression for Autonomous Robot Navigation by Sungjoon Choi, Eunwoo Kim, Kyungjae Lee, and Songhwai Oh
    • Abstract: In this paper, we propose a novel regression method that can incorporate both positive and negative training data into a single regression framework. In detail, a leveraged kernel function for non-stationary Gaussian process regression is proposed. This kernel function can not only vary the correlation between two inputs in the positive direction but also to the negative direction by adjusting a leverage parameter. By using this property, the resulting leveraged non-stationary Gaussian process regression can anchor the regressor to the positive data while avoiding the negative data. We first prove the positive semi-definiteness of the leveraged kernel function using Bochner’s theorem. The mathematical interpretation of the leveraged non-stationary Gaussian process regression using the leveraged kernel function is also addressed. Finally, we apply the leveraged non-stationary Gaussian process regression to the real-time motion control problem. In this case, the positive data refer to what to do and the negative data indicate what not to do. The results show that the controller using both positive and negative data outperforms the controller using positive data only in terms of the collision rate given training sets of the same size.
    • Video
  • Chance-Constrained Target Tracking for Mobile Robots by Yoonseon Oh, Sungjoon Choi, and Songhwai Oh
    • Abstract: This paper presents a robust target tracking algorithm for a mobile robot. A mobile robot is equipped with a sensor with a fan-shaped field of view and finite sensing range. The goal of the mobile robot is to track a moving target such that the probability of losing the target is minimized. We assume that the distribution of the next position of a moving target can be estimated using a motion prediction algorithm. If the next position of a moving target has the Gaussian distribution, the proposed algorithm can guarantee the tracking success probability, i.e., the probability that the next position of the target is within the sensing region of the mobile robot. In addition, the proposed method minimizes the moving distance of the mobile robot based on a bound on the tracking success probability. While the problem considered in this paper is a non-convex optimization problem, we derive analytical solutions which can be easily solved in real-time. The performance of the proposed method is evaluated extensively in simulation and validated in pedestrian following experiments using a Pioneer mobile robot with a Microsoft Kinect sensor.
    • Video
  • Robust Structured Low-Rank Matrix Approximation in Autoregressive Gaussian Process Regression for Autonomous Robot Navigation by Eunwoo Kim, Sungjoon Choi, and Songhwai Oh
    • Abstract: This paper considers the problem of approximating a kernel matrix in an autoregressive Gaussian process regression (AR-GP) in the presence of measurement noises or natural errors for modeling complex motions of pedestrians in a crowded environment. While a number of methods have been proposed to robustly predict future motions of humans, it still remains as a difficult problem in the presence of measurement noises. This paper addresses this issue by proposing a robust structured low-rank matrix approximation method using nuclear-norm regularized l1-norm minimization in AR-GP for robust motion prediction of dynamic obstacles. The proposed method approximates a kernel matrix by finding an orthogonal basis using low-rank symmetric positive semidefinite matrix approximation assuming that a kernel matrix can be well represented by a small number of dominating basis vectors. The proposed method is suitable for predicting the motion of a pedestrian, such that it can be used for safe autonomous robot navigation in a crowd environment. The proposed method is applied to well-known regression data sets and motion prediction problems to demonstrate its robustness and excellent performance compared to existing approaches.
    • Video