LayerRoute: Action-Conditioned Mixture-of-Layers Routing for Vision-Language-Action Policies

2026-09-05

AI Summary

为解决现有VLA接口表示访问灵活性不足的问题，提出LayerRoute方法，通过动态混合VLM层表示和重用动作表示来提高机器人控制任务的性能。

Abstract

Vision-Language-Action (VLA) policies leverage pretrained vision-language models (VLMs) to guide action generation for robot control. VLMs provide hierarchical visual-semantic representations that evolve across layers, from local visual geometry to abstract, language-aligned semantics; different manipulation tasks may therefore require different mixtures of layer representations. Meanwhile, the action module maintains intermediate representations that evolve throughout action computation and may provide useful information for subsequent decisions. However, existing VLA interfaces offer limited flexibility in representation access: VLM information is exposed through fixed layer assignments for each action layer, while intermediate action states are only propagated implicitly through residual streams without explicit reuse. We introduce LayerRoute, an action-conditioned representation routing interface that enables adaptive access to VLM layers and action representations. The Layer Mixture Router dynamically forms mixtures of cached VLM representations, while Action-State Reread reuses earlier action representations. Across diverse simulation and real-world benchmarks, LayerRoute consistently improves StarVLA-$\pi$ and $\pi_{0.5}$, achieving up to 7.2 gains on LIBERO Long with only 0.31% / 3.87% additional parameters. Ablation studies validate the benefit of action-conditioned layer routing, while routing analyses reveal structured allocation patterns across action layers and task settings.

Problem

• Vision-Language-Action
• representation access
• action-conditioned

Innovation

• Action-Conditioned Routing
• Mixture-of-Layers
• Vision-Language-Action Policies
• Representation Reuse