{
  "id": 6101,
  "url": "https://arxiv.org/abs/2604.08544v2",
  "title": "SIM1: Physics-Aligned Simulator as Zero-Shot Data Scaler in Deformable Worlds",
  "summary": "Robotic manipulation with deformable objects represents a data-intensive regime in embodied learning, where shape, contact, and topology co-evolve in ways that far exceed the variability of rigids. Although simulation promises relief from the cost of real-world data acquisition, prevailing sim-to-real pipelines remain rooted in rigid-body abstractions, producing mismatched geometry, fragile soft dynamics, and motion primitives poorly suited for cloth interaction. We posit that simulation fails n",
  "authors": "Yunsong Zhou, Hangxu Liu, Xuekun Jiang, Xing Shen, Yuanzhen Zhou, Hui Wang et al.",
  "category": "research",
  "topics": "agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-09T17:59:52.000Z",
  "fetched_at": "2026-07-14T16:32:15.635Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6101",
  "original_url": "https://arxiv.org/abs/2604.08544v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}