凍結VLAモデルのための低遅延アクション検証の学習
Learning Low-Latency Action Verification for Frozen Vision-Language-Action Models
凍結したVLAモデルの自己展開ログから軽量なアクション検証器を学習し、生成候補の中から成功率の高い行動を低遅延で選択する枠組みを提案した。
著者: Wen-Han Hsieh, Ken Jiankun Zheng, Chia-Chien Chen, David M. Chan, Trevor Darrell
分類: cs.RO, cs.AI, cs.CV
原文アブストラクト
Vision-language-action (VLA) models have made significant progress in general-purpose robotic manipulation. However, sampled action sequences can be ineffective, while evaluating alternatives with computationally expensive verifiers adds latency at each policy query. In this work, we propose HDH, a lightweight framework that uses hindsight from a frozen VLA's own deployments to improve action verification. The framework has three stages. First, we deploy the policy and collect its autonomous rollouts. We then train a compact action verifier to assign each action chunk a predicted task-success score. Finally, the frozen VLA generates action candidates, and the verifier selects the candidate with the highest score for execution. Across four LIBERO-10 tasks, HDH achieves an average success rate of 82.3\% and on three real-world manipulation tasks, HDH improves average success over the baseline policy by 22.6\%.