构建首个面向复杂装配任务的多模态机器人操作数据集。
REASSEMBLE: A Multimodal Dataset for Contact-rich Robotic Assembly and Disassembly
- 基于真实装配板设计，采集4551次操作示范
- 包含4035次成功示范，覆盖781分钟真实操作时长
- 融合事件相机、力矩传感器等多模态数据，支持复杂接触学习
机器人操作仍是机器人领域的核心挑战，尤其在涉及大量物理接触的工业装配与拆卸任务中。现有数据集虽推动了操作学习进展，但主要聚焦于物体重排等简单任务，难以捕捉装配与拆卸中的复杂动态特性。为此，我们提出REASSEMBLE（Robotic assEmbly disASSEMBLy datasEt），一个专为高接触密度操作任务设计的新数据集。该数据集以NIST Assembly Task Board 1为基准，涵盖拾取、插入、移除和放置四类动作，涉及17种物体。共收集4,551次示范，其中4,035次成功，总时长达781分钟。数据集包含多模态传感器信息，包括事件相机、力-扭矩传感器、麦克风及多视角RGB相机。该数据集可支持接触丰富操作学习、任务状态识别、动作分割与任务逆向学习等研究。REASSEMBLE已公开发布于项目网站：https://tuwien-asl.github.io/REASSEMBLE_page/。
原文摘要 · Abstract (English)
Robotic manipulation remains a core challenge in robotics, particularly for contact-rich tasks such as industrial assembly and disassembly. Existing datasets have significantly advanced learning in manipulation but are primarily focused on simpler tasks like object rearrangement, falling short of capturing the complexity and physical dynamics involved in assembly and disassembly. To bridge this gap, we present REASSEMBLE (Robotic assEmbly disASSEMBLy datasEt), a new dataset designed specifically for contact-rich manipulation tasks. Built around the NIST Assembly Task Board 1 benchmark, REASSEMBLE includes four actions (pick, insert, remove, and place) involving 17 objects. The dataset contains 4,551 demonstrations, of which 4,035 were successful, spanning a total of 781 minutes. Our dataset features multi-modal sensor data, including event cameras, force-torque sensors, microphones, and multi-view RGB cameras. This diverse dataset supports research in areas such as learning contact-rich manipulation, task condition identification, action segmentation, and task inversion learning. The REASSEMBLE will be a valuable resource for advancing robotic manipulation in complex, real-world scenarios. The dataset is publicly available on our project website: https://tuwien-asl.github.io/REASSEMBLE_page/.
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