Safety-oriented human–robot collaboration in construction through human preference alignment

Construction faces significant challenges, including high incident rates and shortage of skilled labor. With the sector’s increasing willingness to integrate robotics to improve productivity, this paper addresses the emerging safety challenges in human–robot collaboration (HRC). We present a novel approach that leverages human feedback regarding robot behaviors to define safety-oriented control actions. By training a preference prediction model with human input, we demonstrate the effectiveness of our approach in guiding robots towards safer behaviors in complex construction tasks. The approach begins with designing an online labeling tool tailored for collecting human preference data regarding robot behaviors in collaborative tasks. A score model is then trained to enable prioritization of safer robot behaviors. Finally, safety-oriented robot behaviors can be inferred. This research underscores the importance of aligning construction robot behaviors with human preferences, offering a scalable solution to enhance occupational safety for construction.