机器人工业场景多模态数据集

本数据集适用于具身智能机器人系统在真实物理环境中的技能学习与模型训练，适用条件包括具备机械臂及二指夹爪等末端执行器的操作型机器人平台，支持视觉、关节状态、末端执行器状态等多模态数据同步采集，并满足强时序连续动作控制需求。数据适用范围涵盖具身大模型（如视觉—动作映射模型、端到端策略学习模型等）训练、机器人操作技能学习、工业自动化场景策略优化及多模态融合算法研究，主要面向机器人本体厂商、具身智能算法企业、工业自动化系统集成商及科研机构等对象。本数据集通过真实机器人连续操作过程的高精度时间同步采集，有效解决仿真数据与真实环境差异大、视觉与动作时序错位、多模态耦合不稳定以及复杂操作泛化能力不足等关键问题，为机器人在工业及服务场景中的稳定、自主、连续操作提供高质量数据支撑。This dataset is intended for skill learning and model training of embodied intelligent robotic systems in real physical environments. The applicable conditions include manipulative robotic platforms equipped with end-effectors such as robotic arms and two-finger grippers, supporting synchronized multi-modal data collection including vision, joint states and end-effector states, and meeting the requirements for strong temporal continuous motion control. The applicable scope of the dataset covers training of embodied large models (e.g., vision-action mapping models, end-to-end policy learning models), robotic manipulation skill learning, policy optimization in industrial automation scenarios, and research on multi-modal fusion algorithms. It is mainly targeted at robot hardware manufacturers, embodied AI algorithm enterprises, industrial automation system integrators and research institutions. Collected via high-precision time-synchronized acquisition during continuous real robotic manipulation processes, this dataset effectively addresses key issues such as the large discrepancy between simulation data and real-world environments, temporal misalignment between vision and motion data, unstable multi-modal coupling and insufficient generalization ability for complex manipulation tasks, providing high-quality data support for stable, autonomous and continuous robotic operations in industrial and service scenarios.

搜集汇总

背景与挑战

背景概述

该数据集聚焦于机器人工业场景，整合了视觉、力觉、触觉等多模态感知数据，旨在为机器人在复杂工业环境中的感知、决策与操作提供训练和验证支持。其多模态特性有助于提升机器人对环境的理解能力和任务执行的鲁棒性，适用于智能制造、自动化装配等领域的研究与应用。