arXiv AI
ACCRUE: Continual Off-Policy Reinforcement Learning for Robot Foundation Models
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ACCRUE: Continual Off-Policy Reinforcement Learning for Robot Foundation Models
ACCRUE is a continual off‑policy reinforcement learning framework designed for robot foundation models. It keeps lightweight actor and critic networks across tasks, reusing behavioral and value knowledge from previous RL stages while preserving prior behavior through actor distillation on a small set of retained episodes. Experiments on contact‑rich assembly tasks in simulation and on real robots show that ACCRUE speeds up learning of new tasks, nearly eliminates forgetting, and cuts real‑robot training time by 31.0% to 68.5%.
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