Tactile sequence classification using joint kernel sparse coding

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.