Adaptive interventionIntermediate
UniIntervene++: An Adaptive Intervention Agent for Efficient Real-World Reinforcement Learning
UniIntervene++ is a preprint describing an online learned intervention agent that selects among the robot's own RL policy, trajectory correction, and a task-structured CodePolicy during real-robot reinforcement learning. Across five manipulation tasks on a UR7e arm it reaches 89.67% average success, 6 points above the best baseline, while cutting human-controlled steps to 0.77%. This could reduce the skilled-operator burden needed to make online robot learning practical.