ATLAS: An Annotation Tool for Long-horizon Robotic Action Segmentation

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 pa

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ethics.ai (29 April 2026), “ATLAS: An Annotation Tool for Long-horizon Robotic Action Segmentation,” evidence record 5219, https://ethics.ai/record/5219 (originally published by arXiv).

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