Retrieve-then-Steer: Online Success Memory for Test-Time Adaptation of Generative VLAs

TL;DR

This paper presents a retrieve-then-steer framework for test-time adaptation of generative vision-language-action models, improving robotic task success by leveraging successful past experiences without retraining.

Contribution

It introduces an online success-memory mechanism that retrieves and incorporates environment-specific successful actions into the model during deployment.

Findings

Enhanced task success in simulation and real-world tests.

Improved stability in long-horizon, multi-stage tasks.

Lightweight, non-parametric adaptation without retraining.

Abstract

Vision-Language-Action (VLA) models show strong potential for general-purpose robotic manipulation, yet their closed-loop reliability often degrades under local deployment conditions. Existing evaluations typically treat test episodes as independent zero-shot trials. However, real robots often operate repeatedly in the same or slowly changing environments, where successful executions provide environment-verified evidence of reliable behavior patterns. We study this persistent-deployment setting, asking whether a partially competent frozen VLA can improve its reliability by reusing its successful test-time experience. We propose an online success-memory guided test-time adaptation framework for generative VLAs. During deployment, the robot stores progress-calibrated successful observation-action segments in a long-term memory. At inference, it retrieves state-relevant action chunks, filters inconsistent candidates via trajectory-level consistency, and aggregates them into an elite action prior. To incorporate this prior into action generation, we introduce confidence-adaptive prior guidance, which injects the elite prior into an intermediate state of the flow-matching action sampler and adjusts the guidance strength based on retrieval confidence. This design allows the frozen VLA to exploit environment-specific successful experience while preserving observation-conditioned generative refinement. This retrieve-then-steer mechanism enables lightweight, non-parametric test-time adaptation without requiring parameter updates. Simulation and real-world experiments show improved task success and closed-loop stability, especially in long-horizon and multi-stage tasks.