WISE: ワールドモデル誘導による想像スケジューリングを用いた視覚言語行動モデルの効率的なポストトレーニング
WISE: World-model-guided Imagination Scheduling for Efficient Post-training of Vision-Language-Action Models
ロボット操作のポストトレーニングにおいて、ワールドモデルによる想像を必要な状態でのみ、信頼できる範囲で使用するフレームワークWISEを提案し、計算時間を約80%削減しつつ性能を向上させた。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Chenhao Zhang, Hanyu Zhao, Hang Cheng, Tengfei Pan, Long Zeng
分類: cs.RO
原文アブストラクト
Post-training VLA policies typically rely on supervised fine-tuning with costly expert demonstrations or reinforcement learning with expensive and potentially unstable real-world exploration. World models offer a promising alternative by evaluating candidate behaviors through imagined futures, yet effective post-training requires more than accurate prediction: imagination must be scheduled where it is useful, bounded within reliable horizons, and translated into trustworthy policy supervision. In robotic manipulation, the value of imagination varies substantially across execution stages, while extended rollouts can accumulate prediction errors and introduce unreliable learning signals. We introduce WISE (World-model-guided Imagination Scheduling for Efficient Post-training of Vision-Language-Action Models), a unified framework that coordinates when and how world-model imagination is used during policy refinement. WISE selectively invokes imagination at interaction-relevant states, performs bounded multi-view rollouts, evaluates candidate futures using progress and completion signals, and uses their relative outcomes to refine actions generated from real interaction contexts. Extensive experiments with both $π_0$ and $π_{0.5}$ demonstrate consistent improvements across diverse manipulation tasks while reducing GPU computation time by approximately 80% compared with full imagination. Real-world evaluations further show substantial gains in robustness and generalization under diverse real-world distribution shifts.