Classification of haptic handshake data for the control of human-telerobot social contact interactions

Mobile Remote Presence (MRP) robots have emerged out of the need for telepresence in various settings such as the workplace and hospitals. As with face-to-face experiences, these robot mediated encounters have social aspects that current commercially available MRP robots lack the capabilities to incorporate. In previous work, we integrated a manipulator onto a commercial telerobotic platform to enable expressive gestures and demonstrated that the gesturing capabilities enhanced the social connection between remote and local users. However, we also found that controlling the robot for complex interactions, such as a handshake, diminishes the remote user” s social experience. This paper presents the discovery of models for handshakes in different social contexts, which can be used in a shared-control architecture to reduce the effort on the remote user. Using a haptic measurement glove, force and inertia data was collected for human-human handshakes in various social contexts. By applying a k-nearest neighbor algorithm in combination with dynamic time warping and a support vector machine algorithm, two classification models are derived that predict the social context and can be used in an intelligent shared-control robot architecture.