构建工业级高精度操作数据集，支持多模态感知与控制研究
PRISM: Precision and contact-rich Real-world Industrial Skill dataset with Multimodal sensing
- 采集25+类工业操作任务的5000+轨迹，同步记录多视角视觉、力矩、触觉等数据
- 总时长45小时，涵盖插拔电子元件、传送带分拣等高接触场景
- 面向智能制造中的精准操控，适合机器人多模态学习与工业应用研究
近年来机器人学习的发展得益于日常环境中的大规模数据集。然而，现有数据集多聚焦于短时、低接触任务（如抓取放置），难以体现工业装配所需的高精度控制、力/扭矩调节及触觉反馈。为此，我们提出PRISM，一个大规模多模态数据集，用于高接触型工业操作。该数据集涵盖25项以上操作任务（如电子元件插拔、传送带分拣），覆盖多样机械约束。包含超过5000条轨迹，总计45小时遥操作示范，通过同步多视角RGB-D、力/扭矩、触觉及机器人状态测量获得。相比家庭或实验室数据集，PRISM为高精度工业条件下多模态感知与控制提供了真实基准，是实现真实制造环境中高接触、通用化操作的基础。数据集已开源：https://tengbo-yu.github.io/PRISM/
原文摘要 · Abstract (English)
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/
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