Zobrazeno 1 - 10
of 2 216
pro vyhledávání: '"LIU Xingyu"'
Automated unit test generation has been widely studied, with Large Language Models (LLMs) recently showing significant potential. Moreover, in the context of unit test generation, these tools prioritize high code coverage, often at the expense of pra
Externí odkaz:
http://arxiv.org/abs/2410.13542
Fluorescence molecular tomography (FMT) is a real-time, noninvasive optical imaging technology that plays a significant role in biomedical research. Nevertheless, the ill-posedness of the inverse problem poses huge challenges in FMT reconstructions.
Externí odkaz:
http://arxiv.org/abs/2410.06757
In causal inference, estimating heterogeneous treatment effects (HTE) is critical for identifying how different subgroups respond to interventions, with broad applications in fields such as precision medicine and personalized advertising. Although HT
Externí odkaz:
http://arxiv.org/abs/2407.01004
Autor:
Zhou, Jiehui, Wang, Xumeng, Wong, Kam-Kwai, Zhang, Wei, Liu, Xingyu, Zhang, Juntian, Zhu, Minfeng, Chen, Wei
In causal inference, estimating Heterogeneous Treatment Effects (HTEs) from observational data is critical for understanding how different subgroups respond to treatments, with broad applications such as precision medicine and targeted advertising. H
Externí odkaz:
http://arxiv.org/abs/2407.01893
Autor:
Liu, Jian, Sun, Wei, Yang, Hui, Zeng, Zhiwen, Liu, Chongpei, Zheng, Jin, Liu, Xingyu, Rahmani, Hossein, Sebe, Nicu, Mian, Ajmal
Object pose estimation is a fundamental computer vision problem with broad applications in augmented reality and robotics. Over the past decade, deep learning models, due to their superior accuracy and robustness, have increasingly supplanted convent
Externí odkaz:
http://arxiv.org/abs/2405.07801
We investigate the problem of transferring an expert policy from a source robot to multiple different robots. To solve this problem, we propose a method named $Meta$-$Evolve$ that uses continuous robot evolution to efficiently transfer the policy to
Externí odkaz:
http://arxiv.org/abs/2405.03534
In robotic vision, a de-facto paradigm is to learn in simulated environments and then transfer to real-world applications, which poses an essential challenge in bridging the sim-to-real domain gap. While mainstream works tackle this problem in the RG
Externí odkaz:
http://arxiv.org/abs/2404.03962
Autor:
Lin, Changyi, Liu, Xingyu, Yang, Yuxiang, Niu, Yaru, Yu, Wenhao, Zhang, Tingnan, Tan, Jie, Boots, Byron, Zhao, Ding
Quadrupedal robots have emerged as versatile agents capable of locomoting and manipulating in complex environments. Traditional designs typically rely on the robot's inherent body parts or incorporate top-mounted arms for manipulation tasks. However,
Externí odkaz:
http://arxiv.org/abs/2403.18197
Autor:
Zhang, Ruida, Zhang, Chenyangguang, Di, Yan, Manhardt, Fabian, Liu, Xingyu, Tombari, Federico, Ji, Xiangyang
In this paper, we present KP-RED, a unified KeyPoint-driven REtrieval and Deformation framework that takes object scans as input and jointly retrieves and deforms the most geometrically similar CAD models from a pre-processed database to tightly matc
Externí odkaz:
http://arxiv.org/abs/2403.10099
Publikováno v:
IEEE-RAS International Conference on Soft Robotics (RoboSoft) 2024
We automate soft robotic hand design iteration by co-optimizing design and control policy for dexterous manipulation skills in simulation. Our design iteration pipeline combines genetic algorithms and policy transfer to learn control policies for nea
Externí odkaz:
http://arxiv.org/abs/2403.09933