工业机器人在复杂干扰下的多模态数据集，提升感知与控制鲁棒性。
Liaohe-CobotMagic-PnP: an Imitation Learning Dataset of Intelligent Robot for Industrial Applications
- 融合视觉、力矩与关节状态，同步采集多模态数据
- 包含85%以上几何相似场景与标准化光照梯度
- 适用于工业机器人在动态干扰下的学习与验证
在工业4.0应用中，动态环境干扰导致环境状态与机器人行为间呈现高度非线性且强耦合的交互关系。当前机器人数据集在通过多模态传感器融合有效表征动态环境状态方面仍面临挑战。为此，本文提出一个面向工业应用的多模态干扰数据集，用于复杂条件下机器人感知与控制研究。该数据集整合了尺寸、颜色和光照变化等多维干扰特征，采用高精度传感器同步采集视觉、扭矩及关节状态数据。场景设计包含几何相似度超过85%且具备标准化光照梯度的样本，确保真实世界代表性。基于机器人操作系统（ROS）实现微秒级时间同步与抗振动数据采集协议，保障时序与操作保真度。实验结果表明，该数据集显著提升模型验证鲁棒性，并增强机器人在动态干扰环境中的运行稳定性。数据集已公开：https://modelscope.cn/datasets/Liaoh_LAB/Liaohe-CobotMagic-PnP。
原文摘要 · Abstract (English)
In Industry 4.0 applications, dynamic environmental interference induces highly nonlinear and strongly coupled interactions between the environmental state and robotic behavior. Effectively representing dynamic environmental states through multimodal sensor data fusion remains a critical challenge in current robotic datasets. To address this, an industrial-grade multimodal interference dataset is presented, designed for robotic perception and control under complex conditions. The dataset integrates multi-dimensional interference features including size, color, and lighting variations, and employs high-precision sensors to synchronously collect visual, torque, and joint-state measurements. Scenarios with geometric similarity exceeding 85\% and standardized lighting gradients are included to ensure real-world representativeness. Microsecond-level time-synchronization and vibration-resistant data acquisition protocols, implemented via the Robot Operating System (ROS), guarantee temporal and operational fidelity. Experimental results demonstrate that the dataset enhances model validation robustness and improves robotic operational stability in dynamic, interference-rich environments. The dataset is publicly available at:https://modelscope.cn/datasets/Liaoh_LAB/Liaohe-CobotMagic-PnP.
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