自己改善型VLAポリシー：スプリアスロバストなアクション平滑化のための選択的拡散ノイズ
Self-Improving VLA Policies: Selected Diffusion Noise for Spurious-Robust Action Smoothing
拡散ベースのVLAポリシーのテスト時ロバスト性を高めるため、拡散ノイズ空間を制御可能な自由度として利用し、スプリアスな視覚相関への依存を減らしつつ滑らかなアクションを生成するノイズ選択手法SDNを提案した。
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著者: Duc Minh Nguyen, Bao-Ngoc Dao, Tung M. Luu, Binh Gia Nguyen, Vinh Tong, Anji Liu, Vu N. Duong, Dung D. Le, Daniel Sonntag, Trung Le, Ngan Le, Jan Peter, An Thai Le, Minh Nhat Vu, Mathias Niepert, Khoa D. Doan, Duy M. H. Nguyen, Vien Anh Ngo
分類: cs.RO
原文アブストラクト
Diffusion-based Vision-Language-Action (VLA) policies enable strong generalization in robotic manipulation, but remain sensitive to spurious visual correlations and noisy action generation, leading to brittle behavior under perturbations. We introduce Selected Diffusion Noise (SDN), a simple, training-free test-time method that improves both robustness and success rate by leveraging the diffusion noise space as a controllable degree of freedom. SDN dynamically samples noise vectors that are maximally separated from a reference set to mitigate reliance on spurious cues, while selecting candidates that yield more coherent action trajectories. This dual objective encourages stable behavior even under object-masked observations and reduces action jitter without modifying model parameters. We evaluate SDN on two simulation benchmarks (Google Robot, Widow-X) and two real-world robotic datasets across multiple VLA policies, including pi_0, Groot-N1.5, and Groot-N1.6. SDN consistently improves success rates by +8% in simulation and +10% in real-world settings, while producing smoother and more stable actions. Our results highlight that diffusion noise selection can serve as an effective and general mechanism for enhancing VLA policies at test time.
