  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, anticipate future actions, and synthesize their own motion
accordingly. We explore this within the context of joint action, where 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, where 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 which
did not rely on high-level group behavior, and found our method performed
better both in terms of more closely synchronizing the robot's motion to the
team, and also 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 work will help enable
others in the robotics community to build more fluent and adaptable robots in
the future.
