{
  "@context": "https://schema.org",
  "@type": "article",
  "@id": "https://anchorfact.org/kb/robot-manipulation",
  "headline": "Robot Manipulation: Dexterous Grasping, Sim-to-Real Transfer, and Tactile Sensing",
  "description": "Robot manipulation — the ability to grasp, lift, and manipulate objects — remains one of AI's hardest physical challenges. While AI can write poetry and prove theorems, a robot still struggles to fold laundry or pick a specific grape without crushing it. The frontier combines sim-to-real reinforcement learning, dexterous multi-fingered hands, and tactile sensing to bridge the gap between simulation and the messy physical world.",
  "dateCreated": "2026-05-24T02:49:13.658Z",
  "dateModified": "2026-05-24",
  "author": {
    "@type": "Organization",
    "name": "AnchorFact"
  },
  "publisher": {
    "@type": "Organization",
    "name": "AnchorFact",
    "url": "https://anchorfact.org"
  },
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "anchorfact:confidence": "high",
  "anchorfact:generationMethod": "ai_assisted",
  "citation": [
    {
      "@type": "CreativeWork",
      "name": "Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoid Robots",
      "sameAs": "https://arxiv.org/abs/2502.20396"
    },
    {
      "@type": "CreativeWork",
      "name": "An overview of learning-based dexterous grasping: recent advances, challenges, and future directions",
      "sameAs": "https://link.springer.com/article/10.1007/s10462-025-11262-2"
    }
  ]
}