Defining Fairness in Human-Robot Teams

We seek to understand the human teammate's perception of fairness during a human-robot physical collaborative task where certain subtasks leverage the robot's strengths and others leverage the human's. We conduct a user study (n=30) to investigate the effects of fluency (absent vs. present) and effort (absent vs. present) on participants' perception of fairness. Fluency controls if the robot minimizes the idle time between the human's action and robot's action. Effort controls if the robot performs tasks that it is least skilled at, i.e., most time-consuming tasks, as quickly as possible. We evaluated four human-robot teaming algorithms that consider different levels of fluency and effort. Our results show that effort and fluency help improve fairness without making a trade-off with efficiency. When the robot displays effort, this significantly increased participants' perceived fairness. Participants' perception of fairness is also influenced by team members' skill levels and task type. To that end, we propose three notions of fairness for effective human-robot teamwork: equality of workload, equality of capability, and equality of task type.