106 citations · 194 across the 18 of their papers we have counts for
18 papers
Uncertainty-driven Exploration Strategies for Online Grasp Learning
Yitian Shi, Philipp Schillinger, Miroslav Gabriel +4
Existing grasp prediction approaches are mostly based on offline learning, while, ignoring the exploratory grasp learning during online adaptation to new picking scenarios, i.e., o…
SA6D: Self-Adaptive Few-Shot 6D Pose Estimator for Novel and Occluded Objects
Ning Gao, Ngo Anh Vien, Hanna Ziesche +1
To enable meaningful robotic manipulation of objects in the real-world, 6D pose estimation is one of the critical aspects. Most existing approaches have difficulties to extend pred…
Model-free Grasping with Multi-Suction Cup Grippers for Robotic Bin Picking
Philipp Schillinger, Miroslav Gabriel, Alexander Kuss +2
This paper presents a novel method for model-free prediction of grasp poses for suction grippers with multiple suction cups. Our approach is agnostic to the design of the gripper a…
SyMFM6D: Symmetry-aware Multi-directional Fusion for Multi-View 6D Object Pose Estimation
Fabian Duffhauss, Sebastian Koch, Hanna Ziesche +2
Detecting objects and estimating their 6D poses is essential for automated systems to interact safely with the environment. Most 6D pose estimators, however, rely on a single camer…
Wasserstein Gradient Flows for Optimizing Gaussian Mixture Policies
Hanna Ziesche, Leonel Rozo
Robots often rely on a repertoire of previously-learned motion policies for performing tasks of diverse complexities. When facing unseen task conditions or when new task requiremen…
The e-Bike Motor Assembly: Towards Advanced Robotic Manipulation for Flexible Manufacturing
Leonel Rozo, Andras G. Kupcsik, Philipp Schillinger +9
Robotic manipulation is currently undergoing a profound paradigm shift due to the increasing needs for flexible manufacturing systems, and at the same time, because of the advances…
Deep Black-Box Reinforcement Learning with Movement Primitives
Fabian Otto, Onur Celik, Hongyi Zhou +3
\Episode-based reinforcement learning (ERL) algorithms treat reinforcement learning (RL) as a black-box optimization problem where we learn to select a parameter vector of a contro…
FusionVAE: A Deep Hierarchical Variational Autoencoder for RGB Image Fusion
Fabian Duffhauss, Ngo Anh Vien, Hanna Ziesche +1
Sensor fusion can significantly improve the performance of many computer vision tasks. However, traditional fusion approaches are either not data-driven and cannot exploit prior kn…
Category-Agnostic 6D Pose Estimation with Conditional Neural Processes
Yumeng Li, Ning Gao, Hanna Ziesche +1
We present a novel meta-learning approach for 6D pose estimation on unknown objects. In contrast to ``instance-level" and ``category-level" pose estimation methods, our algorithm l…
Meta-Learning Regrasping Strategies for Physical-Agnostic Objects
Ning Gao, Jingyu Zhang, Ruijie Chen +3
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. I…
What Matters For Meta-Learning Vision Regression Tasks?
Ning Gao, Hanna Ziesche, Ngo Anh Vien +2
Meta-learning is widely used in few-shot classification and function regression due to its ability to quickly adapt to unseen tasks. However, it has not yet been well explored on r…
A Hybrid Approach for Learning to Shift and Grasp with Elaborate Motion Primitives
Zohar Feldman, Hanna Ziesche, Ngo Anh Vien +1
Many possible fields of application of robots in real world settings hinge on the ability of robots to grasp objects. As a result, robot grasping has been an active field of resear…
Residual Feedback Learning for Contact-Rich Manipulation Tasks with Uncertainty
Alireza Ranjbar, Ngo Anh Vien, Hanna Ziesche +2
While classic control theory offers state of the art solutions in many problem scenarios, it is often desired to improve beyond the structure of such solutions and surpass their li…
Differentiable Trust Region Layers for Deep Reinforcement Learning
Fabian Otto, Philipp Becker, Ngo Anh Vien +2
Trust region methods are a popular tool in reinforcement learning as they yield robust policy updates in continuous and discrete action spaces. However, enforcing such trust region…
Pedestrian Behavior Prediction for Automated Driving: Requirements, Metrics, and Relevant Features
Michael Herman, Jörg Wagner, Vishnu Prabhakaran +6
Automated vehicles require a comprehensive understanding of traffic situations to ensure safe and anticipatory driving. In this context, the prediction of pedestrians is particular…
2HDM Higgs-to-Higgs Decays at Next-to-Leading Order
Marcel Krause, Margarete Muhlleitner, Rui Santos +1
The detailed investigation of the Higgs sector at present and future colliders necessitates from the theory side as precise predictions as possible, including higher order correcti…
Gauge-independent Renormalization of the 2-Higgs-Doublet Model
Marcel Krause, Robin Lorenz, Margarete Muhlleitner +2
The 2-Higgs-Doublet Model (2HDM) belongs to the simplest extensions of the Standard Model (SM) Higgs sector that are in accordance with theoretical and experimental constraints. In…
The Order Corrections to the Trilinear Higgs Self-Couplings in the Complex NMSSM
M. Muhlleitner, D. T. Nhung, H. Ziesche
A consistent interpretation of the Higgs data requires the same precision in the Higgs boson masses and in the trilinear Higgs self-couplings, which are related through their commo…