学习对你的VLA说什么：基本无害的视觉语言动作模型引导
Learning What to Say to Your VLA: Mostly Harmless Vision Language Action Model Steering
AI总结：
提出一个框架，通过交互式搜索语言序列改进闭环VLA任务性能，并学习一个改进头预测何时语言引导能提升性能，同时通过共形化防止有害干预。
摘要：
视觉-语言-动作（VLA）模型为机器人控制提供了自然语言接口，但从语言到行为的映射通常脆弱且不直观：语义相似的指令可能引发截然不同的行为，而某些能力可能无法仅通过提示激发。因此，人类指令和零样本语言模型都可能无法可靠地引导VLA成功执行任务。在这项工作中，我们提出了一个框架，该框架交互式地搜索改进闭环VLA任务性能的语言序列，将这些序列提炼为测试时语言反馈策略（LFP），并学习一个改进头来预测何时语言引导会提升性能。我们对这个改进头进行共形化，以防止在分布外场景中LFP相对于原始指令降低任务性能的有害引导干预。关键的是，我们的方法适用于任意冻结的预训练VLA，既不需要访问原始训练分布，也不需要微调底层模型。在已知环境中，我们的共形化LFP在仿真中使基础VLA性能提升24.7%，在硬件中提升65.0%。在视觉和语义扰动下，我们的共形化LFP具有强大的无害性保证，并产生开环提示无法观察到的恢复行为。
英文摘要：
Vision-Language-Action (VLA) models provide a natural language interface to robot control, but the mapping from language to behavior is often brittle and unintuitive: semantically similar instructions can induce drastically different behaviors, while some capabilities may not be elicitable through prompting alone. As a result, both human instructions and zero-shot language models can fail to reliably steer VLAs toward successful task execution. In this work, we propose a framework that interactively searches for language sequences that improve closed-loop VLA task performance, distills these sequences into a test-time language feedback policy (LFP), and learns an improvement head that predicts when language steering will improve performance. We conformalize this improvement head to prevent harmful steering interventions, where the LFP decreases task performance relative to the original instruction on out-of-distribution scenarios. Crucially, our approach operates on arbitrary frozen pre-trained VLAs, requiring neither access to the original training distribution nor fine-tuning of the underlying model. On seen environments, our conformalized LFP improves base VLA performance by 24.7% in simulation and 65.0% in hardware. On visual and semantic perturbations, our conformalized LFP has strong harmlessness guarantees, and produces recovery behaviors not observed with open-loop prompting.
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