{
  "id": 5219,
  "url": "https://arxiv.org/abs/2604.26637v1",
  "title": "ATLAS: An Annotation Tool for Long-horizon Robotic Action Segmentation",
  "summary": "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",
  "authors": "Sergej Stanovcic, Daniel Sliwowski, Dongheui Lee",
  "category": "research",
  "topics": "regulation,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-29T13:03:58.000Z",
  "fetched_at": "2026-07-14T16:31:35.575Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5219",
  "original_url": "https://arxiv.org/abs/2604.26637v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}