Yaofeng Cheng — accepted AI-conference papers

2026

Rethinking Transparent Object Grasping: Depth Completion With Monocular Depth Estimation and Instance Mask

Yaofeng Cheng, Xinkai Gao, Sen Zhang, Chao Zeng, Fusheng Zha, Lining Sun +1

RA-L 2026

Accurate depth maps are essential for robotic grasping. However, transparent objects often cause depth cameras to produce missing or distorted depth due to reflection and refraction, making grasping them particularly challenging. Precise depth estimation for transparent objects is therefore crucial.

Cited by 0 Source ↗code ↗Scholar ↗

2024

A2G: Leveraging Intuitive Physics for Force-Efficient Robotic Grasping

Yaofeng Cheng, Shengkai Liu, Fusheng Zha, Wei Guo, Haoyu Du, Pengfei Wang +1

RA-L 2024

In object manipulation, movements are inherently restricted by object geometry and dynamics. Humans use an intuitive understanding of physics while grasping objects, resulting in an efficient application of manipulation force. This involves a ‘common sense’ awareness of how objects behave in the phy

Cited by 1 Source ↗Scholar ↗