Feedback-Driven Adaptive Task Estimation and Human Error Handling in Human-Robot Collaboration

A human-robot collaboration work system has been developed in which the robot operates following human tasks based on recognition and prediction of human tasks using spatial intelligence. However, the robot's task selection depends on human task recognition, and the robot cannot respond to human error or incorrect recognition of human tasks. In this study, we aim to develop a system that does not depend on the results of human task recognition. The system incorporates human task estimation that accounts for human error and robot motion estimation using reinforcement learning based on human feedback. Its usefulness was evaluated through experiments on human subjects.