Learning What to Say to Your VLA: Mostly Harmless Vision Language Action Model Steering
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 pre...
| Authors | Hyun Joe Jeong, Gokul Swamy, Andrea Bajcsy |
|---|---|
| Primary category | cs.RO |
| Published | 10 June 2026 |
| Last updated at source | 10 June 2026 |
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