ACCRUE: Continual Off-Policy Reinforcement Learning for Robot Foundation Models

ACCRUE enables continual off-policy reinforcement learning for robot foundation models by transferring lightweight actor and critic networks across tasks. This framework mitigates catastrophic forgetting and improves sample efficiency by reusing behavioral and value knowledge from prior RL stages to guide new task learning.