{
  "id": 6557,
  "url": "https://arxiv.org/abs/2603.29419v1",
  "title": "RAAP: Retrieval-Augmented Affordance Prediction with Cross-Image Action Alignment",
  "summary": "Understanding object affordances is essential for enabling robots to perform purposeful and fine-grained interactions in diverse and unstructured environments. However, existing approaches either rely on retrieval, which is fragile due to sparsity and coverage gaps, or on large-scale models, which frequently mislocalize contact points and mispredict post-contact actions when applied to unseen categories, thereby hindering robust generalization. We introduce Retrieval-Augmented Affordance Predict",
  "authors": "Qiyuan Zhuang, He-Yang Xu, Yijun Wang, Xin-Yang Zhao, Yang-Yang Li, Xiu-Shen Wei",
  "category": "research",
  "topics": "safety-alignment,agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-31T08:25:22.000Z",
  "fetched_at": "2026-07-14T16:32:33.103Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6557",
  "original_url": "https://arxiv.org/abs/2603.29419v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}