Search Results for author: Hanna Ziesche

Found 12 papers, 3 papers with code

Uncertainty-driven Exploration Strategies for Online Grasp Learning

no code implementations21 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.

Uncertainty Quantification

SA6D: Self-Adaptive Few-Shot 6D Pose Estimator for Novel and Occluded Objects

no code implementations31 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.

6D Pose Estimation Object

Model-free Grasping with Multi-Suction Cup Grippers for Robotic Bin Picking

no code implementations31 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.

Meta-Learning Regrasping Strategies for Physical-Agnostic Objects

no code implementations23 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.

Friction Meta-Learning

What Matters For Meta-Learning Vision Regression Tasks?

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.

Contrastive Learning Data Augmentation +4

A Hybrid Approach for Learning to Shift and Grasp with Elaborate Motion Primitives

no code implementations2 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.

Data Augmentation

Residual Feedback Learning for Contact-Rich Manipulation Tasks with Uncertainty

no code implementations8 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.

Position Reinforcement Learning (RL)

Pedestrian Behavior Prediction for Automated Driving: Requirements, Metrics, and Relevant Features

no code implementations15 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.

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