机器人感知大升级，轻量化注入几何先验，成功率提升31%

Core Insights - The article discusses the challenges in enabling AI to truly "understand" the 3D world, particularly in the context of visual language action (VLA) models that rely on 2D image-text data [1][2]. Group 1: VLA Model Limitations - Current VLA models lack the necessary 3D spatial understanding for real-world operations, primarily relying on pre-trained visual language models [1]. - Existing enhancement methods based on explicit depth input face deployment difficulties and precision noise issues [1]. Group 2: Evo-0 Model Introduction - Shanghai Jiao Tong University and the University of Cambridge proposed a lightweight method called Evo-0 to enhance the spatial understanding of VLA models by implicitly injecting 3D geometric priors without requiring explicit depth input or additional sensors [2]. - Evo-0 utilizes the Visual Geometry Grounding Transformer (VGGT) to extract 3D structural information from multi-view RGB images, significantly improving spatial perception capabilities [2][3]. Group 3: Model Architecture and Training - Evo-0 integrates VGGT as a spatial encoder, introducing t3^D tokens that contain depth context and cross-view spatial correspondence [3]. - A cross-attention fusion module is employed to merge 2D visual tokens with 3D tokens, enhancing the understanding of spatial structures and object layouts [3][6]. - The model is trained efficiently by only fine-tuning the fusion module, LoRA adaptation layer, and action expert, reducing computational costs [6]. Group 4: Experimental Results - In RLBench simulation tasks, Evo-0 achieved an average success rate improvement of over 28.88% compared to baseline models, particularly excelling in tasks requiring complex spatial relationships [10][11]. - The robustness of Evo-0 was tested under five different interference conditions, consistently outperforming the baseline model pi0 [12][15]. Group 5: Conclusion - Evo-0's key innovation lies in extracting rich spatial semantics through VGGT, bypassing depth estimation errors and sensor requirements, thus enhancing the spatial modeling capabilities of VLA models [16].