PRISM Precision and contact-rich Real-world Industrial Skill Dataset with Multimodal Sensing

Recent progress in robotic learning has been fueled by large-scale datasets collected in everyday environments. However, most existing datasets emphasize short-horizon, low-contact tasks such as pick-and-place, and therefore do not capture the precision control, force/torque or tactile regulation, and multimodal feedback required for industrial assembly. To address this gap, we introduce PRISM, a large-scale multimodal dataset for contact-rich industrial operations.

The dataset spans more than 25 manipulation tasks, including electronic component plug/unplug and conveyor-based sorting, and covers diverse mechanical constraints. PRISM includes more than 5,000 robot trajectories with paired human demonstrations, totaling over 45 hours of data, recorded using synchronized multi-view RGB-D, force/torque, tactile, and robot-state measurements. PRISM provides a realistic benchmark for multimodal perception and control under high-precision industrial constraints, and serves as a foundation for contact-rich, generalizable manipulation in real-world manufacturing environments.