ACCRUE: Continual Off-Policy Reinforcement Learning for Robot Foundation Models
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 upda...
Changyeon Kim, Kimin Lee, Yashraj Narang et al.
· 0 citations