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

2026-07-10 · Source: Artificial Intelligence · Field: Technology & Digital — Robotics & Autonomous Systems, Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

Robo-ValueRL, a unified framework, enables reliable value estimation and systematically traces its effects on policy pretraining and online improvement in offline-to-online reinforcement learning for robotics. It learns a history-conditioned value estimator, evaluating reliability through global-progress and local-preference metrics. These estimates guide quality-conditioned consistency-policy pretraining and a residual adaptation module for online rollouts. Extensive experiments, using 240 hours of offline demonstrations and over 3,000 online trajectories, show a strong association between value reliability and downstream performance. Reliable value functions provide better action-quality estimates, allowing value-guided offline RL to scale more effectively than quality-agnostic behavior cloning, and stabilize online improvement. The system achieved 86% success on millimeter-level precise chip insertion and 84% on generalizable block disassembly.

Key takeaway

For Robotics Engineers developing generalizable manipulation systems, understanding value function reliability is critical. Robo-ValueRL demonstrates that reliable value estimates significantly enhance policy pretraining and stabilize online adaptation, leading to higher success rates. You should prioritize robust value estimation techniques to effectively scale offline reinforcement learning and improve online policy performance in complex robotic tasks.

Key insights

Reliable value estimation is crucial for effective policy optimization in offline-to-online robotic reinforcement learning.

Principles

• Value reliability strongly correlates with policy performance.
• Value-guided RL scales better than quality-agnostic cloning.
• Reliable values stabilize online policy improvement.

Method

Robo-ValueRL learns a history-conditioned value estimator, evaluates reliability via global-progress and local-preference metrics, then propagates estimates into quality-conditioned consistency-policy pretraining and a residual adaptation module for online adaptation.

In practice

• Prioritize high-quality rollout data using value estimates.
• Integrate offline pretraining with online improvement.
• Apply to millimeter-level precise manipulation tasks.

Topics

• Robotics
• Reinforcement Learning
• Offline-to-Online RL
• Value Estimation
• Policy Optimization
• Robotic Manipulation

Best for: Research Scientist, AI Scientist, Robotics Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.