no code implementations • 21 Sep 2023 • Yitian Shi, Philipp Schillinger, Miroslav Gabriel, Alexander Kuss, Zohar Feldman, Hanna Ziesche, Ngo Anh Vien
Existing grasp prediction approaches are mostly based on offline learning, while, ignored the exploratory grasp learning during online adaptation to new picking scenarios, i. e., unseen object portfolio, camera and bin settings etc.
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 • 31 Jul 2023 • Philipp Schillinger, Miroslav Gabriel, Alexander Kuss, Hanna Ziesche, Ngo Anh Vien
This paper presents a novel method for model-free prediction of grasp poses for suction grippers with multiple suction cups.
1 code implementation • 1 Jul 2023 • Fabian Duffhauss, Sebastian Koch, Hanna Ziesche, Ngo Anh Vien, Gerhard Neumann
Detecting objects and estimating their 6D poses is essential for automated systems to interact safely with the environment.
1 code implementation • 18 Oct 2022 • Fabian Otto, Onur Celik, Hongyi Zhou, Hanna Ziesche, Ngo Anh Vien, Gerhard Neumann
In this paper, we present a new algorithm for deep ERL.
no code implementations • 22 Sep 2022 • Fabian Duffhauss, Ngo Anh Vien, Hanna Ziesche, Gerhard Neumann
Sensor fusion can significantly improve the performance of many computer vision tasks.
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 • 2 Nov 2021 • Zohar Feldman, Hanna Ziesche, Ngo Anh Vien, Dotan Di Castro
Many possible fields of application of robots in real world settings hinge on the ability of robots to grasp objects.
no code implementations • 8 Jun 2021 • Alireza Ranjbar, Ngo Anh Vien, Hanna Ziesche, Joschka Boedecker, Gerhard Neumann
We propose a new formulation that addresses these limitations by also modifying the feedback signals to the controller with an RL policy and show superior performance of our approach on a contact-rich peg-insertion task under position and orientation uncertainty.
no code implementations • 15 Dec 2020 • Michael Herman, Jörg Wagner, Vishnu Prabhakaran, Nicolas Möser, Hanna Ziesche, Waleed Ahmed, Lutz Bürkle, Ernst Kloppenburg, Claudius Gläser
In this paper, we thoroughly analyze the requirements on pedestrian behavior prediction for automated driving via a system-level approach.