RetoVLA: Reusing Register Tokens for Spatial Reasoning in Vision-Language-Action Models

Understand

Vision-Language-Action (VLA) models have demonstrated robust performance across diverse robotic tasks.

• However, their high memory and computational demands often limit real-time deployment.
• While existing model compression techniques reduce the parameter footprint, they often drop in 3D spatial reasoning and scene layout understanding.
• This work introduces RetoVLA, an architecture designed to maintain spatial awareness in lightweight models by repurposing Register Tokens-learnable parameters originally introduced to mitigate attention artifacts in Vision Transformers.