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 • 1 Dec 2023 • Yajie Liu, Pu Ge, Haoxiang Ma, Shichao Fan, Qingjie Liu, Di Huang, Yunhong Wang
Referring image segmentation (RIS) aims to segment objects in an image conditioning on free-from text descriptions.
no code implementations • 25 Oct 2023 • Haoxiang Ma, Chongyang Shi, Shuo Han, Michael R. Dorothy, Jie Fu
This paper studies how covert planning can leverage the coupling of stochastic dynamics and the observer's imperfect observation to achieve optimal task performance without being detected.
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.
1 code implementation • 10 Dec 2022 • Haoxiang Ma, Di Huang
Moreover, a Scale Balanced Learning (SBL) loss and an Object Balanced Sampling (OBS) strategy are designed, where SBL enlarges the gradients of the samples whose scales are in low frequency by apriori weights while OBS captures more points on small-scale objects with the help of an auxiliary segmentation network.
Ranked #2 on Robotic Grasping on GraspNet-1Billion
1 code implementation • 27 Oct 2021 • Haoxiang Ma, Hongyu Yang, Di Huang
The recent studies on semantic segmentation are starting to notice the significance of the boundary information, where most approaches see boundaries as the supplement of semantic details.
no code implementations • 30 Nov 2020 • Haoxiang Ma, Jie Fu
By switching between different attention modes, the robot actively perceives task-relevant information to reduce the cost of information acquisition and processing, while achieving near-optimal task performance.