Task decomposition is critical for understanding and learning complex long-horizon manipulation tasks. Especially for tasks involving rich physical interactions, relying solely on visual observations and robot proprioceptive information often fails to reveal the underlying event transitions. This raises the requirement for efficient collection of high-quality multi-modal data as well as robust segmentation method to decompose demonstrations into meaningful modules. Building on the idea of the handheld demonstration device Universal Manipulation Interface (UMI), we introduce TacUMI, a multi-modal data collection system that integrates additionally ViTac sensors, force-torque sensor, and pose tracker into a compact, robot-compatible gripper design, which enables synchronized acquisition of all these modalities during human demonstrations. We then propose a multi-modal segmentation framework that leverages temporal models to detect semantically meaningful event boundaries in sequential manipulations. Evaluation on a challenging cable mounting task shows more than 90 percent segmentation accuracy and highlights a remarkable improvement with more modalities, which validates that TacUMI establishes a practical foundation for both scalable collection and segmentation of multi-modal demonstrations in contact-rich tasks.

翻译：任务分解对于理解和学习复杂的长时程操作任务至关重要。特别是在涉及丰富物理交互的任务中，仅依赖视觉观测和机器人本体感知信息往往无法揭示潜在的事件转换。这提出了高效采集高质量多模态数据以及采用鲁棒分割方法将演示分解为有意义模块的需求。基于手持式演示设备通用操作接口（UMI）的理念，我们提出了TacUMI，这是一个多模态数据采集系统，它将ViTac传感器、力-力矩传感器和位姿跟踪器集成到一个紧凑的、与机器人兼容的夹持器设计中，从而能够在人类演示过程中同步采集所有这些模态的数据。随后，我们提出了一种多模态分割框架，该框架利用时序模型来检测序列化操作中具有语义意义的事件边界。在一个具有挑战性的线缆安装任务上的评估显示，分割准确率超过90%，并突显了更多模态带来的显著改进，这验证了TacUMI为接触密集型任务中多模态演示的可扩展采集与分割奠定了实用基础。