9. Regret-Based Allocation of Autonomy in Shared Visual Detection for Human–Robot Collaborative Assembly in Manufacturing

Appropriate human–robot collaboration (HRC) in assembly in manufacturing will enable more flexible assembly. During the collaboration, the robot needs to make decisions on various issues such as detection of correct assembly parts and correct assembly style to ensure quality, and obstacles to ensure safety. The robot’s decisions may not be reliable due to limitations of the detection systems and disturbances. Human intervention is then necessary though too much involvement of human will increase human workload. Hence, allocation of autonomy through switching between autonomous and manual vision modes seems to be reasonable. Bayesian sequential decision-making can be used to determine the optimal allocation of autonomous and manual modes, but this approach does not fit with human’s decision style, which will result in lack of interests of the human in the collaboration. Human regret plays a critical role in decision-making under uncertainties in detection phenomena. In this case, regret-based suboptimal allocation of autonomous and manual modes in the decision-making is more humanlike, may ensure similar mental models between human and robot and thus a better fit with human psychology, which will potentially improve assembly performance. In this chapter, we include regret in Bayesian decision-making in the detection of correct assembly parts by the robot to provide a risk-based however humanlike decision-making framework, which dynamically switches between autonomous and manual modes in the decision processes in detection phenomena. We then evaluate the effectiveness of the framework for HRC in an assembly task.