{
  "id": 756,
  "url": "https://arxiv.org/abs/2606.21672v1",
  "title": "Imitation from Heterogeneous Demonstrations using Grounded Latent-Action World Models",
  "summary": "Imitation learning has emerged as a powerful paradigm for learning visuomotor policies, but its generalisation and stability are limited by the scale and quality of demonstration data needed. A promising direction is to leverage more abundant but heterogeneous data sources, which differ in action space and often lack action labels altogether. Existing co-training approaches that combine heterogeneous data sources rely on heuristic and hand-engineered alignment techniques. In contrast, we argue t",
  "authors": "Tianyou Wang, Anson Lei, Joe Watson, Ingmar Posner",
  "category": "research",
  "topics": "safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-19T18:23:24.000Z",
  "fetched_at": "2026-07-14T14:14:46.035Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/756",
  "original_url": "https://arxiv.org/abs/2606.21672v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}