LIRA: Local Cross-Layer Information Routing for Vision-Language-Action Decoding
Vision-Language-Action (VLA) models transform representations from pretrained vision-language models (VLMs) into robot actions, yet the interface that routes intermediate VLM features into action decoders remains underexplored. Existing designs either expose only a narrow part of the representation hierarchy or rigidly match each decoder block to one VLM layer, restricting access to complementary task evidence across depths. We introduce LIRA, a local cross-layer action-conditioning mechanism that formulates VLM-to-action conditioning as depth-aware information routing. LIRA operates on task-token features and LIRA Query features derived from intermediate VLM states, then assigns each Par...
| Authors | Zhewei Zhang, Puyue Wang, Guanren Qiao et al. |
|---|---|
| Primary category | cs.RO |
| Published | 6 August 2026 |
| Last updated at source | 6 August 2026 |
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