Industry 5.0 aims to prioritize human operators, focusing on their well-being and capabilities, while promoting collaboration between humans and robots to enhance efficiency and productivity. The integration of collaborative robots must ensure the health and well-being of human operators. 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 following approach (AmPL-RULA) utilizes an Active multi-Preference Learning (AmPL) algorithm, a preference-based 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, Rapid Upper Limb Assessment (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.

中文翻译：

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通过偏好学习和人体工程学增强人机协作框架

工业 5.0 旨在优先考虑人类操作员，关注他们的福祉和能力，同时促进人类与机器人之间的协作，以提高效率和生产力。协作机器人的集成必须确保人类操作员的健康和福祉。事实上，本文解决了对以人为中心的框架的需求，提出了在人机协作（HRC）场景中基于偏好的优化算法，并通过人体工程学评估来改善工作条件。 HRC 应用包括在物体处理任务期间优化协作机器人末端执行器姿势。以下方法 (AmPL-RULA) 采用主动多偏好学习 (AmPL) 算法，这是一种基于偏好的优化方法，其中要求用户通过表达几个候选者之间的成对偏好来迭代地提供定性反馈。为了解决身体健康问题，人体工程学性能指标快速上肢评估 (RULA) 与用户的成对偏好相结合，以便计算出最佳设置。已经进行了实验测试来验证该方法，包括机器人处理物体期间的协作组装。结果表明，所提出的方法可以减轻操作员的体力工作量，同时减轻协作任务的压力。