1 code implementation • 2 Apr 2024 • Haoxiang Ma, Modi shi, Boyang Gao, Di Huang
We focus on the generalization ability of the 6-DoF grasp detection method in this paper.
no code implementations • 18 Mar 2024 • Haoxiang Ma, Ran Qin, Modi shi, Boyang Gao, Di Huang
This paper focuses on the sim-to-real issue of RGB-D grasp detection and formulates it as a domain adaptation problem.
no code implementations • 28 Feb 2023 • Ran Qin, Haoxiang Ma, Boyang Gao, Di Huang
Planar grasp detection is one of the most fundamental tasks to robotic manipulation, and the recent progress of consumer-grade RGB-D sensors enables delivering more comprehensive features from both the texture and shape modalities.
no code implementations • 3 Aug 2021 • Yao Wang, Yangtao Zheng, Boyang Gao, Di Huang
This paper proposes a new deep learning approach to antipodal grasp detection, named Double-Dot Network (DD-Net).
no code implementations • 9 Jan 2018 • Yu-Xing Tang, Josiah Wang, Xiaofang Wang, Boyang Gao, Emmanuel Dellandrea, Robert Gaizauskas, Liming Chen
This is done by modeling the differences between the two on categories with both image-level and bounding box annotations, and transferring this information to convert classifiers to detectors for categories without bounding box annotations.
no code implementations • CVPR 2016 • Yu-Xing Tang, Josiah Wang, Boyang Gao, Emmanuel Dellandrea, Robert Gaizauskas, Liming Chen
This is done by modeling the differences between the two on categories with both image-level and bounding box annotations, and transferring this information to convert classifiers to detectors for categories without bounding box annotations.