PRISM: Precision and contact-rich Real-world Industrial Skill dataset with Multimodal sensing

2026-08-18

AI Summary

为解决工业装配中精准控制和多模态反馈不足的问题，通过收集包含多种操作任务的PRISM数据集，利用多视角RGB-D、力/扭矩、触觉等多模态传感信息。

Abstract

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 (e.g., electronic components plug/unplug, conveyor-based sorting) and covers diverse mechanical constraints. PRISM includes more than 5,000 trajectories totaling 45 hours of teleoperated demonstrations, recorded using synchronized multi-view RGB-D, force/torque, tactile, and robot-state measurements. In contrast to datasets collected in household or laboratory settings, 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. The dataset is open-sourced at: https://tengbo-yu.github.io/PRISM/

Problem

• precision control
• force/torque regulation
• tactile regulation
• multimodal feedback
• industrial assembly

Innovation

• multimodal dataset
• contact-rich operations
• precision control
• force/torque regulation
• tactile feedback