ロボット制御のためのマスク付き生成ポリシー
Masked Generative Policy for Robotic Control
行動を離散トークンとして扱い、条件付きマスク付きトランスフォーマーで並列生成と低信頼トークンの高速洗練を行う視覚運動模倣学習フレームワークMGPを提案。150の操作タスクで成功率向上と最大35倍の推論高速化を実現。
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？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Lipeng Zhuang, Shiyu Fan, Florent P. Audonnet, Yingdong Ru, Edmond S. L. Ho, Gerardo Aragon Camarasa, Paul Henderson
分類: cs.RO, cs.AI
原文アブストラクト
We present Masked Generative Policy (MGP), a novel framework for visuomotor imitation learning. We represent actions as discrete tokens, and train a conditional masked transformer that generates tokens in parallel and then rapidly refines only low-confidence tokens. We further propose two new sampling paradigms: MGP-Short, which performs parallel masked generation with score-based refinement for Markovian tasks, and MGP-Long, which predicts full trajectories in a single pass and dynamically refines low-confidence action tokens based on new observations. With globally coherent prediction and robust adaptive execution capabilities, MGP-Long enables reliable control on complex and non-Markovian tasks that prior methods struggle with. Extensive evaluations on 150 robotic manipulation tasks spanning the Meta-World and LIBERO benchmarks show that MGP achieves both rapid inference and superior success rates compared to state-of-the-art diffusion and autoregressive policies. Specifically, MGP increases the average success rate by 9% across 150 tasks while cutting per-sequence inference time by up to 35x. It further improves the average success rate by 60% in dynamic and missing-observation environments, and solves two non-Markovian scenarios where other state-of-the-art methods fail.
