Temporal Anticipation and Adaptation Methods for Fluent Human-Robot Teaming

Abstract

As robots work with human teams, they will be expected to fluently coordinate with them. While people are adept at coordination and real-time adaptation, robots still lack this skill. In this paper, we introduce TANDEM: Temporal Anticipation and Adaptation for Machines, a series of neurobiologically-inspired algorithms that enable robots to fluently coordinate with people. TANDEM leverages a humanlike understanding of external and internal temporal changes to facilitate coordination. We experimentally validated the approach via a human-robot collaborative drumming task across tempo-changing rhythmic conditions. We found that an adaptation process alone enables a robot to achieve human-level performance. Moreover, by combining anticipatory knowledge along with an adaptation process, robots can potentially perform such tasks better than people. We hope this work will enable researchers to create robots more sensitive to changes in team dynamics.