価値を記憶として：汎用ロボットポリシーのためのオンライン価値誘導型ポストトレーニング
Value as Memory: Online Value-Guided Post-training for General Robot Policies
価値予測を記憶として活用し、オフライン実演とオンライン強化学習を組み合わせることで、汎用ロボットポリシーを実世界で効率的に適応させるフレームワークVaMを提案。
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著者: Wenye Yu, Yijin Chen, Kaiyi Xiong, Wenbo Han, Wendi Chen, Yang Jin, Han Xue, Jun Lv, Cewu Lu, Chuan Wen
分類: cs.RO
原文アブストラクト
Real-world post-training of generalist robot policies faces two challenges: partial observability makes action selection ambiguous, while offline post-training methods delay feedback from deployment experience. We present Value as Memory (VaM), a framework that addresses these challenges through value caching and online real-world reinforcement learning (RL). Value caching conditions actions on recent value predictions as task-progress memory, while online updates enable timely policy improvement by combining offline demonstrations, off-policy interventions, and on-policy rollouts for sample-efficient adaptation. As a result, VaM transitions from offline RL warmup to online autonomous learning with sparse rewards. Across long-horizon tasks involving deformable objects and contact-rich manipulation, VaM yields a mean absolute success-rate gain of $29.4\%$ with value caching, while achieving a $32.8\%$ gain with online autonomous learning over matched offline training. The evaluated online schedule performs $8\times$ as many policy iterations under the same gradient-step budget, with more than $2\times$ lower recorded time per gradient step with concurrent data collection and training.