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版本更新 · v2arXiv:2606.14084

Test-Time Improvement of VLA Policies via Selected Diffusion Noise for Spurious-Robust Action Smoothing

Duc Minh Nguyen, Bao-Ngoc Dao, Tung M. Luu, Binh Gia Nguyen, Vinh Tong, Anji Liu, Vu N. Duong, Dung D. Le 等 18 位作者

原文摘要

Diffusion-based Vision-Language-Action (VLA) policies have demonstrated strong generalization in robot manipulation, but their deployment can remain unreliable due to spurious visual reliance and stochastic action generation, leading to grounding failures and kinematically unstable behaviors. We introduce Selected Diffusion Noise (SDN), a training-free method that treats diffusion noise as a controllable degree of freedom for improving test-time reliability. SDN samples multiple candidate action chunks and applies two-stage selection: it first filters candidates based on task-relevant visual grounding using a counterfactual negative set, then selects the kinematically most stable chunk based on JerkRMS. Experiments on three VLA policies across both simulation and real-world environments show improvements of 8-10% and 18.33%, respectively, demonstrating SDN's effectiveness for test-time improvement.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37215

Retrieve to Act: Motion Primitive Graph Retrieval for Robust VLA Execution

Pham Tri Quang, Bao-Ngoc Dao, Minh Duc Nguyen, Duc-Duy Nguyen, Hung T. Ho, Linh Dang Le, Trong-Bao Ho, Thien-Loc Ha 等 18 位作者

原文摘要

Vision-Language-Action (VLA) policies demonstrate remarkable semantic generalization, yet remain highly vulnerable to out-of-distribution (OOD) visual shifts during execution. While existing test-time methods attempt to suppress spurious visual cues, they fundamentally overlook the rich, uncorrupted kinematic priors embedded in the policy's own training experience. We introduce Retrieve to Act (R2A), a test-time scaling framework that formulates OOD robustness purely as a geometric motion primitive retrieval problem. R2A bypasses fragile visual features by structuring successful training demonstrations into an offline spatiotemporal motion primitive graph. During inference, a query-dependent Graph Neural Network dynamically localizes the robot's ongoing execution within this graph, retrieving structurally compatible future motion primitives. These retrieved geometric priors are then seamlessly harmonized with the frozen VLA's predicted actions via a learned, dimension-wise gating mechanism, adaptively rectifying erroneous spatial trajectories while preserving the policy's local reactivity. Extensive experiments across three state-of-the-art VLA architectures, GR00T N1.7, $π_{0.5}$, and StableVLA, demonstrate that R2A consistently and significantly enhances robustness against diverse contextual perturbations. R2A achieves absolute success rate improvements of up to +12% in simulation benchmarks (LIBERO-Plus, RoboTwin2.0-Plus) and +40% in real-world deployments, without requiring any finetuning of the base policies. Project page: https://retrieve2act.github.io/r2a/

cs.RO · cs.AI

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arXiv ↗PDF ↗