机器人学新任务不忘旧技能，训练时间砍掉近七成
机器人学新任务时，最怕“学了新的忘了旧的”。这篇论文给机器人基础模型加了一套“随身笔记本”：每学完一个任务，就把学到的行为和价值判断压缩成轻量模型，带到下一个任务里接着用；学新任务时，旧技能靠一小段回放数据“蒸馏”保住，几乎零遗忘。结果是在真实机器人上，后续任务的训练时间平均缩短31%到68.5%。这不是你明天能上手用的东西，但它指向一个方向：机器人不再每次从零学起，而是像人一样带着经验越学越快。
📄 原文摘要(英文)
Reinforcement learning (RL) post-training improves robot foundation models (RFMs) beyond their demonstrations, but real-world deployment demands more: learning new tasks efficiently while retaining previously acquired behaviors. Existing continual RL methods on top of VLAs mitigate forgetting but rely on on-policy updates with limited forward transfer, whereas off-policy methods improve single-task sample efficiency without addressing learner persistence across tasks. We propose ACCRUE, a continual off-policy RL framework that carries lightweight actor and critic networks across tasks, allowing each stage to reuse behavioral and value knowledge acquired through prior RL. The inherited critic adapts only on current-task transitions and guides current-task actor learning, while prior behavior is preserved by distilling the actor toward cached targets on a small number of retained episodes. Fixed visual features keep the inherited learner and its replay compatible with base-policy updates. Across contact-rich assembly sequences in simulation and on real-robot hardware, ACCRUE accelerates subsequent-task learning with near-zero forgetting and significantly reduces real-robot training time by 31.0\% to 68.5\%.