2 citations · 4 across the 6 of their papers we have counts for
7 papers
Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding
Han Xiao, Yina Xie, Guanxin Tan +12
Visual Document Understanding has become essential with the increase of text-rich visual content. This field poses significant challenges due to the need for effective integration…
EnerVerse: Envisioning Embodied Future Space for Robotics Manipulation
Siyuan Huang, Liliang Chen, Pengfei Zhou +8
We introduce EnerVerse, a generative robotics foundation model that constructs and interprets embodied spaces. EnerVerse employs a chunk-wise autoregressive video diffusion framewo…
SKT: Integrating State-Aware Keypoint Trajectories with Vision-Language Models for Robotic Garment Manipulation
Xin Li, Siyuan Huang, Qiaojun Yu +6
Automating garment manipulation poses a significant challenge for assistive robotics due to the diverse and deformable nature of garments. Traditional approaches typically require…
UniAff: A Unified Representation of Affordances for Tool Usage and Articulation with Vision-Language Models
Qiaojun Yu, Siyuan Huang, Xibin Yuan +9
Previous studies on robotic manipulation are based on a limited understanding of the underlying 3D motion constraints and affordances. To address these challenges, we propose a com…
A3VLM: Actionable Articulation-Aware Vision Language Model
Siyuan Huang, Haonan Chang, Yuhan Liu +5
Vision Language Models (VLMs) have received significant attention in recent years in the robotics community. VLMs are shown to be able to perform complex visual reasoning and scene…
Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want
Weifeng Lin, Xinyu Wei, Ruichuan An +6
In this paper, we present the Draw-and-Understand framework, exploring how to integrate visual prompting understanding capabilities into Multimodal Large Language Models (MLLMs). V…
ManipVQA: Injecting Robotic Affordance and Physically Grounded Information into Multi-Modal Large Language Models
Siyuan Huang, Iaroslav Ponomarenko, Zhengkai Jiang +5
While the integration of Multi-modal Large Language Models (MLLMs) with robotic systems has significantly improved robots' ability to understand and execute natural language instru…