242 citations · 280 across the 9 of their papers we have counts for
5 papers · 1 filter
Unknown Object Grasping for Assistive Robotics
Elle Miller, Maximilian Durner, Matthias Humt +5
We propose a novel pipeline for unknown object grasping in shared robotic autonomy scenarios. State-of-the-art methods for fully autonomous scenarios are typically learning-based a…
Combining Shape Completion and Grasp Prediction for Fast and Versatile Grasping with a Multi-Fingered Hand
Matthias Humt, Dominik Winkelbauer, Ulrich Hillenbrand +1
Grasping objects with limited or no prior knowledge about them is a highly relevant skill in assistive robotics. Still, in this general setting, it has remained an open problem, es…
Virtual Reality via Object Pose Estimation and Active Learning: Realizing Telepresence Robots with Aerial Manipulation Capabilities
Jongseok Lee, Ribin Balachandran, Konstantin Kondak +6
This article presents a novel telepresence system for advancing aerial manipulation in dynamic and unstructured environments. The proposed system not only features a haptic device,…
Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes
Jongseok Lee, Jianxiang Feng, Matthias Humt +2
This paper presents a probabilistic framework to obtain both reliable and fast uncertainty estimates for predictions with Deep Neural Networks (DNNs). Our main contribution is a pr…
Bayesian Optimization Meets Laplace Approximation for Robotic Introspection
Matthias Humt, Jongseok Lee, Rudolph Triebel
In robotics, deep learning (DL) methods are used more and more widely, but their general inability to provide reliable confidence estimates will ultimately lead to fragile and unre…