Efficient human-robot collaboration: When should a robot take initiative?

Abstract

The promise of robots assisting humans in everyday tasks has led to a variety of research questions and challenges in human-robot collaboration. Here, we address the question of whether and when a robot should take initiative during joint human-robot task execution. We designed a robotic system capable of autonomously performing table-top manipulation tasks while monitoring the environmental state. Our system is able to predict future environmental states and the robot’s actions to reach them using a dynamic Bayesian network. To evaluate our system, we implemented three different initiative conditions to trigger the robot’s actions. Human-initiated help gives control of the robot action timing to the user; robot-initiated reactive help triggers robot assistance when it detects that the human needs help; robot-initiated proactive help makes the robot help whenever it can. We performed a user study (N=18) to compare the trigger mechanisms in terms of quality of interaction, system performance and perceived sociality of the robot. We found that people collaborate best with a proactive robot, yielding better team fluency and high subjective ratings. However, they prefer having control of when the robot should help, rather than working with a reactive robot that only helps when needed. We also found that participants gazed at the robot’s face more during the human-initiated help compared to the other conditions. This shows that asking for the robot’s help may lead to a more “social” interaction, without improving the quality of interaction or the system performance.