Agentic Reinforcement Learning Framework Improves Robotic Manipulation Robustness

Jul 16, 2026

A new agentic reinforcement learning framework enables robots to restore effective execution in manipulation tasks by making high-level decisions based on recent execution history. The approach introduces runtime metrics to assess execution quality and triggers recovery mechanisms when deviations occur. Evaluated on the LIBERO benchmark, the method achieves up to 13.7% higher success rates under standard conditions and up to 39.2% under disturbance conditions, demonstrating improved robustness.

Why it matters: Improving execution robustness in robotic manipulation is crucial for reliable performance in uncertain and dynamic environments.

Full story at: arXiv Robotics ↗