{
  "id": 6774,
  "url": "https://arxiv.org/abs/2603.24083v1",
  "title": "Knowledge-Guided Manipulation Using Multi-Task Reinforcement Learning",
  "summary": "This paper introduces Knowledge Graph based Massively Multi-task Model-based Policy Optimization (KG-M3PO), a framework for multi-task robotic manipulation in partially observable settings that unifies Perception, Knowledge, and Policy. The method augments egocentric vision with an online 3D scene graph that grounds open-vocabulary detections into a metric, relational representation. A dynamic-relation mechanism updates spatial, containment, and affordance edges at every step, and a graph neural",
  "authors": "Aditya Narendra, Mukhammadrizo Maribjonov, Dmitry Makarov, Dmitry Yudin, Aleksandr Panov",
  "category": "research",
  "topics": "regulation,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-25T08:41:32.000Z",
  "fetched_at": "2026-07-14T16:32:45.892Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6774",
  "original_url": "https://arxiv.org/abs/2603.24083v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}