no code implementations • 31 Aug 2023 • Ning Gao, Ngo Anh Vien, Hanna Ziesche, Gerhard Neumann
To enable meaningful robotic manipulation of objects in the real-world, 6D pose estimation is one of the critical aspects.
no code implementations • 22 Aug 2023 • Ning Gao, Bernard Hohmann, Gerhard Neumann
In our work, we initialize the slot representations with clustering algorithms conditioned on the perceptual input features.
1 code implementation • 25 May 2023 • Yun Yue, Jiadi Jiang, Zhiling Ye, Ning Gao, Yongchao Liu, Ke Zhang
Deep Neural Networks (DNNs) generalization is known to be closely related to the flatness of minima, leading to the development of Sharpness-Aware Minimization (SAM) for seeking flatter minima and better generalization.
no code implementations • 12 Aug 2022 • Ning Gao, Dantong Li, Anchit Mishra, Junchen Yan, Kyrylo Simonov, Giulio Chiribella
The MED provides a metric on the space of von Neumann measurements, and can be efficiently estimated by letting the measurement processes act in an indefinite order, using a setup known as the quantum switch, which also allows one to quantify the noncommutativity of arbitrary quantum processes.
no code implementations • 14 Jun 2022 • Yumeng Li, Ning Gao, Hanna Ziesche, Gerhard Neumann
We present a novel meta-learning approach for 6D pose estimation on unknown objects.
no code implementations • 23 May 2022 • Ning Gao, Jingyu Zhang, Ruijie Chen, Ngo Anh Vien, Hanna Ziesche, Gerhard Neumann
Grasping inhomogeneous objects in real-world applications remains a challenging task due to the unknown physical properties such as mass distribution and coefficient of friction.
2 code implementations • CVPR 2022 • Ning Gao, Hanna Ziesche, Ngo Anh Vien, Michael Volpp, Gerhard Neumann
To this end, we (i) exhaustively evaluate common meta-learning techniques on these tasks, and (ii) quantitatively analyze the effect of various deep learning techniques commonly used in recent meta-learning algorithms in order to strengthen the generalization capability: data augmentation, domain randomization, task augmentation and meta-regularization.
no code implementations • 4 May 2020 • Ning Gao, Yong Zeng, Jian Wang, Di wu, Chaoyue Zhang, Qingheng Song, Jiachen Qian, Shi Jin
In this paper, via extensive flight experiments, we aim to firstly validate the recently derived theoretical energy model for rotary-wing UAVs, and then develop a general model for those complicated flight scenarios where rigorous theoretical model derivation is quite challenging, if not impossible.