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This study investigated the effects of different approach directions, movement speeds, and trajectories of a co-robot’s end-effector on workers’ mental stress during handover tasks.

Human–robot collaboration (HRC) is gaining attention in industry and academia. Understanding robot-related factors causing mental stress is crucial for designing collaborative tasks that minimize workers’ stress.

Mental stress in HRC tasks was measured subjectively through self-reports and objectively through galvanic skin response (GSR) and electromyography (EMG). Robot-related factors including approach direction, movement speed, and trajectory were analyzed.

Movement speed and approach direction had significant effects on subjective ratings, EMG, and GSR. High-speed and approaching from one side consistently resulted in higher fear, lower comfort, and predictability, as well as increased EMG and GSR signals, indicating higher mental stress. Movement trajectory affected GSR, with the sudden stop condition eliciting a stronger response compared to the constrained trajectory. Interaction effects between speed and approach direction were observed for “surprise” and “predictability” subjective ratings. At high speed, approach direction did not significantly differ, but at low speeds, approaching from the side was found to be more surprising and unpredictable compared to approaching from the front.

The mental stress of workers during HRC is lower when the robot’s end effector (1) approaches a worker within the worker’s field of view, (2) approaches at a lower speed, or (3) follows a constrained trajectory.

The outcome of this study can serve as a guide to design HRC tasks with a low level of workers’ mental stress.

This study aims to improve workers’ postures and thus reduce the risk of musculoskeletal disorders in human-robot collaboration by developing a novel model-free reinforcement learning method.

Human-robot collaboration has been a flourishing work configuration in recent years. Yet, it could lead to work-related musculoskeletal disorders if the collaborative tasks result in awkward postures for workers.

The proposed approach follows two steps: first, a 3D human skeleton reconstruction method was adopted to calculate workers’ continuous awkward posture (CAP) score; second, an online gradient-based reinforcement learning algorithm was designed to dynamically improve workers’ CAP score by adjusting the positions and orientations of the robot end effector.

In an empirical experiment, the proposed approach can significantly improve the CAP scores of the participants during a human-robot collaboration task when compared with the scenarios where robot and participants worked together at a fixed position or at the individual elbow height. The questionnaire outcomes also showed that the working posture resulted from the proposed approach was preferred by the participants.

The proposed model-free reinforcement learning method can learn the optimal worker postures without the need for specific biomechanical models. The data-driven nature of this method can make it adaptive to provide personalized optimal work posture.

The proposed method can be applied to improve the occupational safety in robot-implemented factories. Specifically, the personalized robot working positions and orientations can proactively reduce exposure to awkward postures that increase the risk of musculoskeletal disorders. The algorithm can also reactively protect workers by reducing the workload in specific joints.