{
  "id": 5074,
  "url": "https://arxiv.org/abs/2605.01483v1",
  "title": "Research on Vision-Language Question Answering Models for Industrial Robots",
  "summary": "A hierarchical cross-modal fusion model is proposed for vision-language question answering (VLQA) in industrial robotics, targeting the challenges of semantic ambiguity, complex environmental layouts, and domain-specific terminology common in modern manufacturing. The framework integrates advanced object detection, multi-scale visual encoding, syntactic parsing, and task-aware semantic attention to unite vision and language signals into a joint reasoning space. Region-based deep networks extract",
  "authors": "Ping Li, Bartlomiej Brzozka",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-02T15:11:48.000Z",
  "fetched_at": "2026-07-14T16:31:31.209Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5074",
  "original_url": "https://arxiv.org/abs/2605.01483v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}