ROVE: Unlocking Human Interventions for Humanoid Manipulation via Reinforcement Learning
ROVE improves humanoid manipulation policies by learning from imperfect human corrections during deployment. It uses a human-in-the-loop system and Optimistic Value Estimation (OVE) to extract high-value behaviors from mixed data. This leads to consistently better policies through an iterative closed-loop process, significantly boosting success rates on real tasks.