When Life Gives You BC, Make Q-functions: Extracting Q-values from Behavior Cloning for On-Robot Reinforcement Learning
arXiv:2605.05172v2 Announce Type: replace-cross Abstract: Behavior Cloning (BC) has emerged as a highly effective paradigm for robot learning. However, BC lacks a self-guided mechanism for online improvement after demonstrations have been collected. Existing offline-to-online learning methods often cause policies to replace previously learned good actions due to a distribution mismatch between offline data and online learning. In this work, we propose Q2RL, Q-Estimation and Q-Gating from BC for Reinforcement Learning, an algorithm for efficient offline-to-online learning. Our method consists o
This research addresses a critical limitation in current robot learning paradigms, specifically the challenge of efficiently transitioning from offline learned behaviors to robust online adaptation.
Improved methods for integrating offline behavior cloning with online reinforcement learning will accelerate the development and deployment of autonomous robotic systems, making them more capable in real-world, dynamic environments.
The proposed Q2RL algorithm provides a self-guided mechanism for robots to refine learned behaviors online without catastrophic forgetting, significantly enhancing the practical applicability of reinforcement learning in robotics.
- · Robotics companies
- · Automation sector
- · AI researchers
- · Developers reliant on purely offline learning
- · Industries with high deployment costs for unadaptive robots
Robots will be able to learn and adapt more effectively in complex, unstructured environments with less human intervention.
This improved adaptability could reduce the cost and increase the versatility of robotic deployments across various industries like manufacturing, logistics, and service.
More capable and easily deployable robots could accelerate the timeline for widespread commercialisation of advanced robotic systems, including humanoid robots.
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Read at arXiv cs.AI