{
  "id": 19455,
  "url": "https://arxiv.org/abs/2608.12650v1",
  "title": "Attune: A Self-Annotation Tool for Understanding Robot Operator Attention Profiles",
  "summary": "Deploying robot fleets in complex, real-world environments requires human operators to supervise multiple robots simultaneously. Managing operator attention is a fundamental challenge of designing multi-robot supervision interfaces, encompassing both feed layout and feed content (i.e., robot behavior design). Thus far, designers lack empirical guidance on the latter-how to change a robot's behavior to capture, sustain, or relinquish operator attention during multi-robot supervision. In our visio",
  "authors": "Puqi Zhou, Sungsoo Ray Hong, David Porfirio",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-12T23:20:29.000Z",
  "fetched_at": "2026-08-14T05:10:49.168Z",
  "source_slug": "x-arxiv-cs-hc",
  "source_name": "arXiv cs.HC",
  "source_homepage": "https://arxiv.org/list/cs.HC/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/19455",
  "original_url": "https://arxiv.org/abs/2608.12650v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}