Design and Evaluation of a Human-Comfort-Aware Robot Behavior Controller
Yuchen Yan, Yunyi Jia · 2025
The study addresses the challenge of enhancing human comfort during human-robot collaboration (HRC) tasks, where misalignment between robot behaviors and human comfort preferences is common. Current approaches often fail to adapt robot behavior effectively according to human preferences, leading to discomfort and less satisfying collaboration experience. To address this limitation, a multi-linear regression-based comfort prediction model is proposed to fine-tune robot behavioral factors and improve human comfort responses. In the experiment, three HRC object-delivery tasks were configured with high, medium, and low comfort levels based on different robot behaviors. Five behavioral factors—delivery distance, speed, height, trajectory, and gripper pose were varied, and participants rated their comfort on a five-point Likert scale. The results showed that the overall and factor-based comfort ratings largely aligned with the expected comfort levels of the tasks, indicating the model’s reliability in predicting and enhancing human comfort.