{
  "id": 13766,
  "url": "https://arxiv.org/abs/2607.21655",
  "title": "Progress Reward Modeling for Robotic Learning: A Comprehensive Survey",
  "summary": "Robotic learning takes place in dynamic environments with large behavior spaces. A terminal success signal only tells the robot whether the task is completed. It does not explain whether the current behavior is making progress, remaining unchanged, or undoing earlier progress. For this reason, recent studies have increasingly explored progress rewards that provide feedback during task execution. However, the current literature lacks a shared framework. Existing methods use different observations",
  "authors": "Jianshu Zhang, Keliang Wu, Haoran Lu, Anbang Liu, Ce Zhang, Weijie Yin",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-21T20:00:00.000Z",
  "fetched_at": "2026-07-28T05:10:12.325Z",
  "source_slug": "hf-daily",
  "source_name": "HuggingFace Daily Papers",
  "source_homepage": "https://huggingface.co/papers",
  "ethics_ai_record_url": "https://ethics.ai/record/13766",
  "original_url": "https://arxiv.org/abs/2607.21655",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}