多様な経験によるスケーリング：視覚言語行動モデルのための手法
Scaling by Diversified Experience for Vision-Language-Action Models
視覚言語行動モデルの実世界展開における課題を解決するため、意図分離アルゴリズムと類似サンプル誘導型強化学習を導入し、ロボットタスクとマルチモーダルベンチマークで優れた性能を達成した。
著者: Leiyu Wang, Zhaofengnian Wang, Xueqi Li, Luoyi Fan, Cewu Lu, Nanyang Ye
分類: cs.CV
原文アブストラクト
Vision-Language-Action models face significant challenges in real-world deployment due to the entanglement of high-level reasoning with low-level control, and the instability of policy optimization. In this paper, we introduce SyVLA, a robust VLA model trained with diversified experiences. We propose an Intention Decoupling algorithm to isolate control-relevant features from reasoning contexts and a similar-sample guided RL pipeline to stabilize policy updates and mitigate distribution shift. Extensive experiments on real-world robotic tasks and multi-modal benchmarks demonstrate that SyVLA achieves superior task success rates and stronger out-of-distribution generalization compared to existing methods, while effectively preserving core vision-language capabilities. Codes and Datasets is released on \href{https://sy-vla.github.io/}{project page}.