多段階一貫性統合ショートカットモデルに基づく模倣学習ポリシー
Imitation Learning Policy based on Multi-Step Consistent Integration Shortcut Model
フローマッチングの推論速度を改善するため、ショートカットモデルに多段階一貫性損失と適応的勾配配分を導入し、1ステップ推論で高性能を実現した模倣学習手法を提案。
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？
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6. 次に読むべき論文は？
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著者: Yu Fang, Xinyu Wang, Xuehe Zhang, Wanli Xue, Mingwei Zhang, Shengyong Chen, Jie Zhao
分類: cs.RO
原文アブストラクト
The wide application of flow-matching methods has greatly promoted the development of robot imitation learning. However, these methods all face the problem of high inference time. To address this issue, researchers have proposed distillation methods and consistency methods, but the performance of these methods still struggles to compete with that of the original diffusion models and flow-matching models. In this article, we propose a one-step shortcut method with multi-step integration for robot imitation learning. To balance the inference speed and performance, we extend the multi-step consistency loss on the basis of the shortcut model, split the one-step loss into multi-step losses, and improve the performance of one-step inference. Secondly, to solve the problem of unstable optimization of the multi-step loss and the original flow-matching loss, we propose an adaptive gradient allocation method to enhance the stability of the learning process. Finally, we evaluate the proposed method in two simulation benchmarks and five real-world environment tasks. The experimental results verify the effectiveness of the proposed algorithm.
