We present LiHRA, a novel dataset designed to facilitate the development of automated, learning-based, or classical risk monitoring (RM) methods for Human-Robot Interaction (HRI) scenarios. The growing prevalence of collaborative robots in industrial environments has increased the need for reliable safety systems. However, the lack of high-quality datasets that capture realistic human-robot interactions, including potentially dangerous events, slows development. LiHRA addresses this challenge by providing a comprehensive, multi-modal dataset combining 3D LiDAR point clouds, human body keypoints, and robot joint states, capturing the complete spatial and dynamic context of human-robot collaboration. This combination of modalities allows for precise tracking of human movement, robot actions, and environmental conditions, enabling accurate RM during collaborative tasks. The LiHRA dataset covers six representative HRI scenarios involving collaborative and coexistent tasks, object handovers, and surface polishing, with safe and hazardous versions of each scenario. In total, the data set includes 4,431 labeled point clouds recorded at 10 Hz, providing a rich resource for training and benchmarking classical and AI-driven RM algorithms. Finally, to demonstrate LiHRA's utility, we introduce an RM method that quantifies the risk level in each scenario over time. This method leverages contextual information, including robot states and the dynamic model of the robot. With its combination of high-resolution LiDAR data, precise human tracking, robot state data, and realistic collision events, LiHRA offers an essential foundation for future research into real-time RM and adaptive safety strategies in human-robot workspaces.
Plahl et al. (2025) studied this question.