Annotating long-horizon robotic demonstrations with precise temporal action boundaries is crucial for training and evaluating action segmentation and manipulation policy learning methods. Existing annotation tools, however, are often limited: they are designed primarily for vision-only data, do not natively support synchronized visualization of robot-specific time-series signals (e.g., gripper state or force/torque), or require substantial effort to adapt to different dataset formats. In this paper, we introduce ATLAS, an annotation tool tailored for long-horizon robotic action segmentation. ATLAS provides time-synchronized visualization of multi-modal robotic data, including multi-view video and proprioceptive signals, and supports annotation of action boundaries, action labels, and task outcomes. The tool natively handles widely used robotics dataset formats such as ROS bags and the Reinforcement Learning Dataset (RLDS) format, and provides direct support for specific datasets such as REASSEMBLE. ATLAS can be easily extended to new formats via a modular dataset abstraction layer. Its keyboard-centric interface minimizes annotation effort and improves efficiency. In experiments on a contact-rich assembly task, ATLAS reduced the average per-action annotation time by at least 6% compared to ELAN, while the inclusion of time-series data improved temporal alignment with expert annotations by more than 2.8% and decreased boundary error fivefold compared to vision-only annotation tools.

翻译：对长程机器人演示进行精确的时间动作边界标注，对于训练和评估动作分割及操作策略学习方法至关重要。然而，现有标注工具往往存在局限：它们主要针对纯视觉数据设计，不支持机器人特有的时间序列信号（如夹爪状态或力/力矩）的同步可视化，或者需要大量工作才能适应不同数据集格式。本文介绍了ATLAS，一种专为长程机器人动作分割定制的标注工具。ATLAS提供多模态机器人数据的时间同步可视化，包括多视角视频和本体感知信号，并支持动作边界、动作标签和任务结果的标注。该工具原生支持广泛使用的机器人数据集格式，如ROS bag和强化学习数据集（RLDS）格式，并直接支持特定数据集（如REASSEMBLE）。通过模块化数据集抽象层，ATLAS可轻松扩展至新格式。其以键盘为中心的界面最大限度地减少了标注工作量并提高了效率。在接触密集型装配任务的实验中，与ELAN相比，ATLAS将每次动作的平均标注时间减少了至少6%，同时通过包含时间序列数据，将时间对齐与专家标注的差异降低了超过2.8%，并将边界误差降低至纯视觉标注工具的五分之一。