ドリフトベース方策最適化：オンラインロボット制御のためのネイティブ一段階方策学習
Drift-Based Policy Optimization: Native One-Step Policy Learning for Online Robot Control
多段階生成方策の推論コストを削減するため、固定点ドリフト目的で訓練時に反復精錬を内部化した一段階生成方策（DBP）と、そのオンラインRLフレームワーク（DBPO）を提案した。
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？
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著者: Yuxuan Gao, Yedong Shen, Shiqi Zhang, Wenhao Yu, Yifan Duan, Jia pan, Jiajia Wu, Jiajun Deng, Yanyong Zhang
分類: cs.RO
原文アブストラクト
Although multi-step generative policies achieve strong performance in robotic manipulation by modeling multimodal action distributions, they require multi-step iterative denoising at inference time. Each action therefore needs tens to hundreds of network function evaluations (NFEs), making them costly for high-frequency closed-loop control and online reinforcement learning (RL). To address this limitation, we propose a two-stage framework for native one-step generative policies that shifts refinement from inference to training. First, we introduce the Drift-Based Policy (DBP), which leverages fixed-point drifting objectives to internalize iterative refinement into the model parameters, yielding a one-step generative backbone by design while preserving multimodal action modeling capacity. Second, we develop Drift-Based Policy Optimization (DBPO), an online RL framework that equips the pretrained backbone with a compatible stochastic interface, enabling stable on-policy updates without sacrificing the one-step deployment property. Extensive experiments demonstrate the effectiveness of the proposed framework across offline imitation learning, online fine-tuning, and real-world control scenarios. DBP matches or exceeds the performance of multi-step diffusion policies while achieving up to $100\times$ faster inference. It also consistently outperforms existing one-step baselines on challenging manipulation benchmarks. Moreover, DBPO enables effective and stable policy improvement in online settings. Experiments on a real-world dual-arm robot demonstrate reliable high-frequency control at 105.2 Hz.
