Movement Coordination in Human–Robot Teams: A Dynamical Systems Approach

In order to be effective teammates, robots need to be able to understand high-level human behavior to recognize, anticipate, and adapt to human motion. We have designed a new approach to enable robots to perceive human group motion in real time to anticipate future actions and synthesize their own motion accordingly. We explore this within the context of joint action, in which humans and robots move together synchronously. In this paper we present an anticipation method, which takes high-level group behavior into account. We validate the method within a human-robot interaction scenario, in which an autonomous mobile robot observes a team of human dancers and then successfully and contingently coordinates its movements to “join the dance.” We compared the results of our anticipation method to move the robot with another method that did not rely on high-level group behavior and found that our method performed better both in terms of more closely synchronizing the robot's motion to the team and exhibiting more contingent and fluent motion. These findings suggest that the robot performs better when it has an understanding of high-level group behavior than when it does not. This study will help enable others in the robotics community to build more fluent and adaptable robots in the future.