Sim-to-Real ManipulationIntermediate
Skill2Real: Agentic Skill Learning for Zero-Shot Sim-to-Real Robot Manipulation
Skill2Real is a preprint describing an agentic framework that learns reusable robot manipulation skills in simulation and transfers them zero-shot to real hardware through a shared code-based policy interface. On a physical UR5e, it raises mean task completion from 27.50% with no learned skills to 78.75% across four tasks, and on unseen long-horizon LIBERO-Pro Long tasks it raises GPT-6 Astra success from 2.0% to 56.3% using frozen skills. This matters because it shifts sim-to-real learning from adapting low-level representations to acquiring validated, composable task knowledge without real-robot fine-tuning.