Q-OIL: Optimistic Exploration in Learning from Human Interventions
Overview
This research addresses the challenge of robot learning with human feedback by analyzing real-world intervention data, which reveals that humans often intervene when progress stalls and may provide suboptimal corrections. The authors propose Q-OIL, a method that uses a progress-based model of human behavior to guide exploration in reinforcement learning. By adding optimistic bonuses to transitions involving human corrections and employing decoupled critics for stability, Q-OIL achieves 10-50% higher success rates compared to baselines in both simulation and on real robots, demonstrating improved performance on pretrained vision-language-action models for bimanual manipulation tasks.
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Environment: simulation
“arXiv:2611.08576v1 Announce Type: new Abstract: As robots are deployed in the real world, they will inevitably make mistakes. Human interventions provide valuable feedback for improving their behavior, enabling them to adapt online to new scenarios. However, studying human-in-the-loop robot learning is difficult: real-world experiments are slow and expensive, so most algorithm development relies on simulated human mo”
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- arXiv Robotics — research abstractsSignalQ-OIL: Optimistic Exploration in Learning from Human Interventions
This research addresses the challenge of robot learning with human feedback by analyzing real-world intervention data, which reveals that humans often intervene when progress stalls and may provide suboptimal corrections. The authors propose Q-OIL, a method that uses a progress-based model of human behavior to guide exploration in reinforcement learning. By adding optimistic bonuses to transitions involving human corrections and employing decoupled critics for stability, Q-OIL achieves 10-50% higher success rates compared to baselines in both simulation and on real robots, demonstrating improved performance on pretrained vision-language-action models for bimanual manipulation tasks.
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