5 citations
- Chinese University of Hong Kong, ShenzhenCN2 papers
- Harbin Institute of TechnologyCN1 paper
- Horizon Robotics (China)1 paper
- Nanjing UniversityCN1 paper
- Nankai UniversityCN1 paper
- Omnitech Robotics (United States)US1 paper
- Shandong UniversityCN1 paper
- State Key Laboratory of Robotics and SystemsCN1 paper
- Suzhou University of Science and TechnologyCN1 paper
4 papers
A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks
Zichun Xu, Zhaomin Wang, Yuntao Li +4
For peg-in-hole tasks, humans rely on binocular visual perception to locate the peg above the hole surface and then proceed with insertion. This paper draws insights from this beha…
DreamLifting: A Plug-in Module Lifting MV Diffusion Models for 3D Asset Generation
Ze-Xin Yin, Jiaxiong Qiu, Liu Liu +5
The labor- and experience-intensive creation of 3D assets with physically based rendering (PBR) materials demands an autonomous 3D asset creation pipeline. However, most existing 3…
Recruiting Heterogeneous Crowdsource Vehicles for Updating a High-definition Map
Wentao Ye, Yuan Luo, Bo Liu +1
The high-definition map is a cornerstone of autonomous driving. Unlike constructing a costly fleet of mapping vehicles, the crowdsourcing paradigm is a cost-effective way to keep a…
Efficient and Cost-effective Vehicle Recruitment for HD Map Crowdsourcing
Wentao Ye, Yuan Luo, Bo Liu +1
The high-definition (HD) map is a cornerstone of autonomous driving. The crowdsourcing paradigm is a cost-effective way to keep an HD map up-to-date. Current HD map crowdsourcing m…