HyperVLA: Efficient Inference in Vision-Language-Action Models via Hypernetworks

Understand

Built upon language and vision foundation models with strong generalization ability and trained on large-scale robotic data, Vision-Language-Action (VLA) models have recently emerged as a promising approach to learning generalist robotic policies.

• However, a key drawback of existing VLAs is their extremely high inference costs.
• In this paper, we propose HyperVLA to address this problem.
• Unlike existing monolithic VLAs that activate the whole model during both training and inference, HyperVLA uses a novel hypernetwork (HN)-based architecture that activates only a small task-specific policy during inference, while still retaining the high model capacity needed to accommodate diverse multi-task behaviors during training.