arXiv Machine Learning June 11, 2026 By Haoyuan Deng, Yitong Gao, Yudong Lin, Haichao Liu, Zhenyu Wu, Ziwei Wang

UniIntervene: Agentic Intervention for Efficient Real-World Reinforcement Learning

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arXiv:2606. 12372v1 Announce Type: cross Abstract: Human-in-the-loop reinforcement learning (HiL-RL) has emerged as an effective paradigm for real-world robotic manipulation, enabling online policy improvement with human guidance.

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Related stories

From Pretraining to Proficiency: Real-World Subtask RL for Long-Horizon Manipulation with Minimal Human Intervention

BEE: Intervention-Adaptive Real-World Reinforcement Learning with Vision-Language-Action Models

E2HiL: Entropy-Guided Sample Selection for Efficient Real-World Human-in-the-Loop Reinforcement Learning

Support-Constrained RL Enables Real-World Policy Improvement without Real-World Experience

Robo-ValueRL: Reliable Value Estimation for Offline-to-Online Reinforcement Learning

ROVE: Unlocking Human Interventions for Humanoid Manipulation via Reinforcement Learning