UC San Diego
Coordination Dynamics in Human-Robot Teams
- Author(s): Iqbal, Tariq
- Advisor(s): Riek, Laurel D
- et al.
As robots become more common in our daily lives, they will be expected to interact with and work with teams of people. If a robot has an understanding of the underlying dynamics of a team, then it can recognize, anticipate, and adapt to human motion to be a more effective teammate.
To enable robots to understand team dynamics, I developed a new, non-linear method to detect group synchronization, which takes multiple types of discrete, task-level events into consideration. I explored this method within the context of coordinated action and validated it by applying it to both human-only and mixed human-robot teams. The results suggest that our method is more accurate in estimating group synchronization than other methods from the literature.
Building on this work, I designed a new method for robots to perceive human group behavior in real-time, anticipate future actions, and synthesize their motion accordingly.
I validated this approach within a human-robot interaction scenario, where a robot successfully and contingently coordinated with people in real-time. We found that robots perform better when they have an understanding of team dynamics than they do not.
Moreover, I investigated how the presence and behavior of robots affect group coordination in multi-human, multi-robot teams. The results suggested that group coordination was significantly degraded when a robot joined a human-only group, and was further degraded when a second robot joined the team and employed a different anticipation algorithm from the other robot. These findings suggest that heterogeneous behavior of robots in a multi-human group can play a major role in how group coordination dynamics change.
Furthermore, I designed and implemented algorithms for robots to coordinate with people in tempo-changing environments. These algorithms leveraged a human-like understanding of temporal anticipation and adaptation during the coordination process. I validated the algorithms by applying them in a human-robot drumming scenario. The results suggest that an adaptation process alone enables a robot to achieve human-level performance. Moreover, by combining anticipatory knowledge (anticipation algorithm), along with an adaptation process, a robot can be even better than people in both uniform and single tempo-changing conditions.
My research will enable robots to recognize, anticipate, and adapt to human groups. This work will help enable others in the robotics community to build more fluent and adaptable robots in the future, and provide a necessary understanding for how we design future human-robot teams.