论文/arXiv 2609.30092·1 个月前

用于鲁棒机器人部署的自适应 VLA

针对VLA在部署时硬件偏移下易失效的问题，Self-Adaptive VLA用自身轨迹上下文调制策略，并在四个精密操作任务上验证。

AI 整理

• 硬件零部件
• 数据与采集
• 具身模型

原文链接：Self-Adaptive VLA for Robust Robot Deployment（arXiv 2609.30092 · 发布日期：2026-09-24）

Self-Adaptive VLA for Robust Robot Deployment

针对VLA在部署时硬件偏移下易失效的问题，Self-Adaptive VLA用自身轨迹上下文调制策略，并在四个精密操作任务上验证。

核心要点

• 无记忆VLA在测试时环境变化，尤其硬件磨损或校准不准下脆弱。
• 方法采集注入硬件偏移的策略轨迹，将专家动作预补偿为偏移条件示范。
• 轻量上下文编码器压缩视觉、本体和动作，经AdaLN用潜变量调制策略。
• 四个精密双臂和灵巧操作任务中，在偏移下恢复基础策略超80%性能。

验证范围与局限：验证范围为摘要所述四个精密双臂和灵巧操作任务、硬件偏移及新工作站部署。

英文摘要 (Abstract)

While Vision-Language-Action (VLA) models demonstrate impressive capabilities in robotic manipulation, their memoryless nature renders them brittle to test-time environment shifts, particularly hardware shifts caused by wear or imperfect calibration. Enabling these models to self-adapt during deployment without requiring continuous on-site recalibration remains a critical bottleneck for real-world scalability. In this work, we introduce Self-Adaptive VLA, a novel post-training recipe that enables the policy to iteratively adapt to deployment-time hardware shifts leveraging its own rollouts as context. To do so, we first collect policy rollouts under deliberately injected hardware shifts. We then transform the base policy's training data into shift-conditioned expert demonstrations by pre-compensating the expert actions for these known shifts. Next, we introduce a lightweight, plug-in context encoder that compresses the context, including visual observation, proprioception, and actions in the shifted environment, into a latent context token. This token modulates the policy through adaptive layer normalization (AdaLN). Furthermore, we find that context tokens can be ensembled, allowing the policy to iteratively self-correct and mitigate failures step by step. Extensive experiments across four precision-critical bi-manual and dexterous manipulation tasks show that Self-Adaptive VLA recovers over 80% of the base policy's performance under hardware shifts, such as actuation bias and joint encoder offsets. Moreover, Self-Adaptive VLA enables more robust deployment to new workstations compared to the base policy. Our approach provides a pathway for robust large-scale real-world robot deployments and easier maintenance. See videos at https://icefoxzhx.github.io/self-adaptive-vla.

作者：Hongxin Zhang, Chunru Lin, Tsun-Hsuan Johnson Wang, Zhenjia Xu, Chuang Gan

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2609.30092：Self-Adaptive VLA for Robust Robot Deployment访问 2026-10-21