UniIntervene++: An Adaptive Intervention Agent for Efficient Real-World Reinforcement Learning

arXiv:2610.03620 · cs.LG, cs.RO · Submitted 2026-10-02 · Read on arXiv

cs.LG, cs.RO

Submitted: 2026-10-02

Updated: 2026-10-02

Comments: Yudong Lin and Haoyuan Deng contributed equally. Ziwei Wang is the corresponding author. Code is available in our \href{https://github.com/dannyyudong/An-Adaptive-Intervention-Agent-for-Efficient-Real-World-Reinforcement-Learning}{GitHub repository}

Code: https://github.com/dannyyudong/An-Adaptive-Intervention-Agent-for-Efficient-Real-World-Reinforcement-Learning

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Reinforcement Learning — 1× in this paper · explained in 4 episodes

Sources

• Facet-0: A Robotic Foundation Model for Contact-Rich Precise Manipulation
• Failure-Aware RL: Reliable Offline-to-Online Reinforcement Learning with Self-Recovery for Real-World Manipulation
• Uncertainty Comes for Free: Human-in-the-Loop Policies with Diffusion Models
• Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning
• One Demonstration Is Enough for Real-World Robotic Reinforcement Learning
• UniIntervene: Agentic Intervention for Efficient Real-World Reinforcement Learning
• OHP-RL: Online Human Preference as Guidance in Reinforcement Learning for Robot Manipulation
• Real-world Reinforcement Learning from Suboptimal Interventions
• WorldSample: Closed-loop Real-robot RL with World Modelling

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