7 papers
3D-Aware VLMs with Implicit and Explicit Geometries
Wenhao Li, Xueying Jiang, Quanhao Qian +4
Despite rapid progress, most existing vision-language models (VLMs) built from 2D visual inputs often struggle when handling various 3D tasks that require fine-grained spatial unde…
GeoProp: Grounding Robot State in Vision for Generalist Manipulation
Guoyang Zhao, Quanhao Qian, Gongjie Zhang +5
Proprioception is fundamental to robotic manipulation, yet standard fusion methods often treat it as an isolated vector lacking explicit alignment with visual tokens. Without a dir…
From Fixed to Free Cameras: Calibration-Free View-Robust Vision-Language-Action Model
Wenhao Li, Xueying Jiang, Quanhao Qian +4
Real-world robot deployment rarely maintains the training-stage camera setup, where cameras often experience repositioning or remounting depending on actual scenarios. Existing vie…
On the Generalization Capacities of MLLMs for Spatial Intelligence
Gongjie Zhang, Wenhao Li, Quanhao Qian +4
Multimodal Large Language Models (MLLMs) that directly process RGB inputs for tasks like 3D localization and navigation have shown remarkable potential. However, we argue that thes…
RoboSVG: A Unified Framework for Interactive SVG Generation with Multi-modal Guidance
Jiuniu Wang, Gongjie Zhang, Quanhao Qian +3
Scalable Vector Graphics (SVGs) are fundamental to digital design and robot control, encoding not only visual structure but also motion paths in interactive drawings. In this work,…
GP3: A 3D Geometry-Aware Policy with Multi-View Images for Robotic Manipulation
Quanhao Qian, Guoyang Zhao, Gongjie Zhang +4
Effective robotic manipulation relies on a precise understanding of 3D scene geometry, and one of the most straightforward ways to acquire such geometry is through multi-view obser…