Shangqing Mao — accepted AI-conference papers

2026

RealAppiance: Let High-fidelity Appliance Assets Controllable and Workable as Aligned Real Manauls

Yuzheng Gao, Yuxing Long, Lei Kang, Yuchong Guo, Ziyan Yu, Shangqing Mao +5

CVPR 2026

Existing appliance assets suffer from poor rendering, incomplete mechanisms, and misalignment with manuals, leading to simulation-reality gaps that hinder appliance manipulation development. In this work, we introduce the RealAppliance dataset, comprising 100 high-fidelity appliances with complete p

Cited by 0 Source ↗Scholar ↗

2025

RwoR: Generating Robot Demonstrations from Human Hand Collection for Policy Learning without Robot

Liang Heng, Xiaoqi Li, Shangqing Mao, Jiaming Liu, Ruolin Liu, Jingli Wei +6

IROS 2025

Recent advancements in imitation learning have shown promising results in robotic manipulation, driven by the availability of high-quality training data. To improve data collection efficiency, some approaches focus on developing specialized teleoperation devices for robot control, while others direc

Cited by 2 Source ↗code ↗Scholar ↗