A Framework for Human-Robot Collaboration Enhanced by Preference Learning and Ergonomics

Industry 5.0 aims to prioritize the needs and capabilities of human operators and design and implement production environments that support their role. Indeed, this paper addresses the need for a human-centered framework proposing a preference-based optimization algorithm in a human-robot collaboration (HRC) scenario with an ergonomics assessment to improve working conditions. The HRC application consists of optimizing a collaborative robot end-effector pose during an object-handling task. The approach utilizes an Active multi-Preference Learning (AmPL) algorithm, a preferencebased optimization method, where the user is requested to iteratively provide qualitative feedback by expressing pairwise preferences between a couple of candidates. To address physical well-being, an ergonomic performance index (RULA) is combined with the user’s pairwise preferences, so that the optimal setting can be computed. Experimental tests have been conducted to validate the method, involving collaborative assembly during the object handling performed by the robot. Results illustrate that the proposed method can improve the physical workload of the operator while easing the collaborative task.