Robot Perception Upgraded: Geometric Priors Boost Success 31%
Boost robot success rates with Evo-0: a lightweight method that injects 3D geometric priors into AI models, eliminating the need for extra sensors.
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In the field of robot learning, enabling AI to truly “understand” the 3D world has always been a challenge.
VLA models, typically built on pretrained vision-language models (VLMs), are trained solely on 2D image-text data, lacking the 3D spatial understanding required for real-world operations.
Current augmentation methods based on explicit depth inputs are effective but rely on additional sensors or depth estimation networks, introducing issues such as deployment complexity and accuracy noise.
To address this, Shanghai Jiao Tong University and the University of Cambridge proposed a lightweight method, Evo-0, to enhance the spatial understanding of vision-language-action (VLA) models by implicitly injecting 3D geometric priors without requiring explicit depth inputs or additional sensors.