Super-Flexible Skin Sensors Embedded on the Whole Body, Self-Organizing Based on Haptic Interactions

As robots become more ubiquitous in our daily lives, humans and robots are working in ever-closer physical proximity to each other. These close physical distances change the nature of human robot interaction considerably. First, it becomes more important to consider safety, in case robots accidentally touch (or hit) the humans. Second, touch (or haptic) feedback from humans can be a useful additional channel for communication, and is a particularly natural one for humans to utilize. Covering the whole robot body with malleable tactile sensors can help to address the safety issues while providing a new communication interface. First, soft, compliant surfaces are less dangerous in the event of accidental human contact. Second, flexible sensors are capable of distinguishing many different types of touch (e.g., hard v.s. gentle stroking). Since soft skin on a robot tends to invite humans to engage in even more touch interactions, it is doubly important that the robot can process haptic feedback from humans. In this paper, we discuss attempts to solve some of the difficult new technical and information processing challenges presented by flexible touch sensitive skin. Our approach is based on a method for sensors to self-organize into sensor banks for classification of touch interactions. This is useful for distributed processing and helps to reduce the maintenance problems of manually configuring large numbers of sensors. We found that using sparse sensor banks containing as little as 15% of the full sensor set it is possible to classify interaction scenarios with accuracy up to 80% in a 15-way forced choice task. Visualization of the learned subspaces shows that, for many categories of touch interactions, the learned sensor banks are composed mainly of physically local sensor groups. These results are promising and suggest that our proposed method can be effectively used for automatic analysis of touch behaviors in more complex tasks.