Robotics
LIRA: Local Cross-Layer Information Routing for Vision-Language-Action Decoding
A novel depth-aware routing mechanism improves robot manipulation accuracy and distribution shift robustness.
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...
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ArXiv CS.RO
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ArXiv CS.RO
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LIRA optimizes robot performance by allowing vision-language models to dynamically access multi-layered visual information during action planning. This modular approach significantly boosts zero-shot generalization capabilities without requiring expensive retraining of the underlying model architecture.
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