The problem of place recognition is central to robot navigation. The robot needs to be able to recognize or at least to be able to estimate the likelihood that it has been at a place before when it has returned to a previously visited place. We cast the place recognition problem as one of classifying among multiple linear regression models, and argue that new theory from sparse signal representation offers the key to addressing the problem. In this paper, a joint kernel sparse coding model is developed to tackle the multivariate sonar samples place recognition problem. The experimental results show that the joint sparse coding achieves better performance than 1-Nearest Neighborhood (1-NN) method.

Tactile sensors in the robotic fingertips are used to capture multiple object properties such as texture, roughness, spatial features, compliance or friction and therefore becomes a very important sense modality for intelligent robot. However, existing work neglects the intrinsic relation between different fingers which simultaneously contact the object. In this paper, a joint kernel sparse coding model is developed to tackle the multi-finger tactile sequence classification problem. In this model, the intrinsic relations between fingers are explicitly considered using the joint sparse coding which encourages different modal coding to share the same support. The experimental results show that the joint sparse coding achieves better performance than conventional sparse coding.

In the task of grasping objects with robotic hand, power grasp, also called whole hand grasp, is usually adopted. However, power grasp may incur severe target object deformation. To address this issue, a hybrid control algorithm is proposed, which could control dexterous robotic hand at different stages. When robotic hand does not contact target object, the speed of the fingers increases sharply under the position control law, guaranteeing the rapidness of the system. During the close contact process, the features of tactile data are analyzed and matrix models that corresponds to different types of objects are set up; and then corresponding integral separation PID control law is designed meticulously. The proposed method is verified by the experiment platform, showing a promising performance.

In this paper, we propose a co-learning particle filter approach for vehicle tracking, which is very important for intelligent vehicle. The proposal distribution of the particle filter is a combination of an extra support vector machine (SVM) detector and the motion prior. Previous works focusing on how to online update the detector or the observation likelihood using the tracking results. These approaches belong to ¿self-learning¿ fashion and easily tend to drift. The major difference between the proposed approach and previous works is that the SVM detector and the likelihood function can be mutually updated in a co-learning manner. By adopting the co-learning technology, the unlabelled samples which are generated during tracking are utilized to progressively modify the SVM detector and update the observation likelihood; therefore the resulting tracker is more robust and effectively avoids the drift problem. Finally, the performance of the proposed approach is evaluated using extensive real visual tracking examples.