Detecting and Synthesizing Synchronous Joint Action in Human-Robot Teams

Abstract

To become capable teammates to people, robots need the ability to interpret human activities and appropriately adjust their actions in real time. The goal of our research is to build robots that can work fluently and contingently with human teams. To this end, we have designed novel nonlinear dynamical methods to automatically model and detect synchronous joint action (SJA) in human teams. We also have extended this work to enable robots to move jointly with human teammates in real time. In this paper, we describe our work to date, and discuss our future research plans to further explore this research space. The results of this work are expected to benefit researchers in social signal processing, human-machine interaction, and robotics.