ACCRUE: Continual Off-Policy Reinforcement Learning for Robo
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Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: ACCRUE: Continual Off-Policy Reinforcement Learning for Robot Foundation Models. This paper presents ACCRUE, a continual off-policy reinforcement learning framework designed to improve robot foundation models by enabling efficient learning of new tasks while retaining previously acquired behaviors. T…
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- arXiv Robotics — research abstractsACCRUE: Continual Off-Policy Reinforcement Learning for Robot Foundation Models
This paper presents ACCRUE, a continual off-policy reinforcement learning framework designed to improve robot foundation models by enabling efficient learning of new tasks while retaining previously acquired behaviors. The approach uses lightweight actor and critic networks, distillation of prior behavior, and fixed visual features to maintain compatibility with base-policy updates, achieving near-zero forgetting and reducing real-robot training time by 31.0% to 68.5%.
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