RoboTacDex：用于人形机器人操作的灵巧视觉-触觉-动作数据集
RoboTacDex: A Dexterous Visual-Tactile-Action Dataset for Humanoid Manipulation
AI总结：
提出RoboTacDex，一个包含6k轨迹、19个任务、23种技能和22个物体的大规模多模态灵巧操作数据集，基于Unitree G1人形机器人构建，提供多视角RGB-D、触觉反馈和语义标注，并评估了三种模仿学习模型。
摘要：
在机器人学习领域，大规模且多样化的示范轨迹为提升机器人操作能力提供了基础。我们介绍了RoboTacDex，这是一个由人形机器人执行的大规模、多模态且多样化的灵巧操作行为数据集。该数据集基于公开可访问的人形机器人Unitree G1构建，包含覆盖19个任务、23种技能以及与22个物体交互的6k条轨迹。RoboTacDex提供了全面的记录，包括多视角RGB和深度信息、触觉反馈以及详细的语义标注。此外，该数据集包含一系列相对具有挑战性的任务，这些任务只能通过双臂和灵巧手完成，旨在模仿类人操作逻辑并模拟真实世界的操作复杂性。为确保数据收集质量，我们开发了一种改进的多摄像头同步系统，以实现毫秒级的数据同步和模态记录。在我们的实验中，我们在数据集上评估了三种具有代表性的模仿学习模型，分析了它们的性能以及在不同任务类别中的各自优势和局限性。在一系列任务中的成功试验结果和适度的泛化能力表明了所收集数据集的有效性和多样性。我们的数据集将很快开源。
英文摘要：
In the field of robot learning, large-scale and diverse demonstration trajectories provide the fundamental basis for enhancing robotic manipulation ability. We introduce RoboTacDex, a large, multi-modal, and diverse dataset of dexterous manipulation behaviors performed with a humanoid robot. Built on the publicly accessible humanoid robot Unitree G1, RoboTacDex consists of 6k trajectories covering 19 tasks, 23 skills, and interactions with 22 objects. RoboTacDex provides comprehensive records including multi-view RGB and depth information, tactile feedback, and detailed semantic annotations. Furthermore, the dataset features a variety of relatively challenging tasks that can only be completed by dual arms and dexterous hands, aiming to mimic human-like operational logic and simulate real-world manipulation complexity. To ensure data collection quality, we develop an improved multi-camera synchronization system to enable millisecond data synchronization and recording of modalities. In our experiments, we evaluate three representative imitation learning models on our dataset, analyzing their performance as well as their respective strengths and limitations across different task categories. Successful trial results and a moderate level of generalization capabilities across a suite of tasks indicate the effectiveness and diversity of the collected dataset. Our dataset will be open-sourced soon.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
