4 citations · 7 across the 11 of their papers we have counts for
11 papers
AffordTrajDP: Dynamic Affordance-Guided Visuomotor Policy Learning for Robotic Manipulation
Gaoyuan Wu, Ziyu Shan, Haoyang Du +2
Affordance-guided imitation learning has shown impressive performance in robotic manipulation tasks by compressing visual perception into task-specific geometric constraints (e.g.,…
DVG-WM: Disentangled Video Generation Enables Efficient Embodied World Model for Robotic Manipulation
Ziyu Shan, Zhenyu Wu, Xiaofeng Wang +2
Video-based embodied world models provide an appealing substrate for robotic manipulation by predicting future states, yet current approaches remain limited by a fundamental entang…
DockAnywhere: Data-Efficient Visuomotor Policy Learning for Mobile Manipulation via Novel Demonstration Generation
Ziyu Shan, Yuheng Zhou, Gaoyuan Wu +3
Mobile manipulation is a fundamental capability that enables robots to interact in expansive environments such as homes and factories. Most existing approaches follow a two-stage p…
RoCo Challenge at AAAI 2026: Benchmarking Robotic Collaborative Manipulation for Assembly Towards Industrial Automation
Haichao Liu, Yuheng Zhou, Zhenyu Wu +14
Embodied Artificial Intelligence (EAI) is rapidly developing, gradually subverting previous autonomous systems' paradigms from isolated perception to integrated, continuous action.…
Point Cloud Compression and Objective Quality Assessment: A Survey
Yiling Xu, Yujie Zhang, Shuting Xia +6
The rapid growth of 3D point cloud data, driven by applications in autonomous driving, robotics, and immersive environments, has led to criticals demand for efficient compression a…
CLIP-PCQA: Exploring Subjective-Aligned Vision-Language Modeling for Point Cloud Quality Assessment
Yating Liu, Yujie Zhang, Ziyu Shan +1
In recent years, No-Reference Point Cloud Quality Assessment (NR-PCQA) research has achieved significant progress. However, existing methods mostly seek a direct mapping function f…
Learning Disentangled Representations for Perceptual Point Cloud Quality Assessment via Mutual Information Minimization
Ziyu Shan, Yujie Zhang, Yipeng Liu +1
No-Reference Point Cloud Quality Assessment (NR-PCQA) aims to objectively assess the human perceptual quality of point clouds without relying on pristine-quality point clouds for r…
Asynchronous Feedback Network for Perceptual Point Cloud Quality Assessment
Yujie Zhang, Qi Yang, Ziyu Shan +1
Recent years have witnessed the success of the deep learning-based technique in research of no-reference point cloud quality assessment (NR-PCQA). For a more accurate quality predi…
Contrastive Pre-Training with Multi-View Fusion for No-Reference Point Cloud Quality Assessment
Ziyu Shan, Yujie Zhang, Qi Yang +5
No-reference point cloud quality assessment (NR-PCQA) aims to automatically evaluate the perceptual quality of distorted point clouds without available reference, which have achiev…
PAME: Self-Supervised Masked Autoencoder for No-Reference Point Cloud Quality Assessment
Ziyu Shan, Yujie Zhang, Qi Yang +3
No-reference point cloud quality assessment (NR-PCQA) aims to automatically predict the perceptual quality of point clouds without reference, which has achieved remarkable performa…
GPA-Net:No-Reference Point Cloud Quality Assessment with Multi-task Graph Convolutional Network
Ziyu Shan, Qi Yang, Rui Ye +4
With the rapid development of 3D vision, point cloud has become an increasingly popular 3D visual media content. Due to the irregular structure, point cloud has posed novel challen…