242 citations · 248 across the 16 of their papers we have counts for
18 papers
SPIRIT: Perceptive Shared Autonomy for Robust Robotic Manipulation under Deep Learning Uncertainty
Jongseok Lee, Ribin Balachandran, Harsimran Singh +6
Deep learning (DL) has enabled impressive advances in robotic perception, yet its limited robustness and lack of interpretability hinder reliable deployment in safety critical appl…
Evaluating Latent Generative Paradigms for High-Fidelity 3D Shape Completion from a Single Depth Image
Matthias Humt, Ulrich Hillenbrand, Rudolph Triebel
While generative models have seen significant adoption across a wide range of data modalities, including 3D data, a consensus on which model is best suited for which task has yet t…
Human-Interpretable Uncertainty Explanations for Point Cloud Registration
Johannes A. Gaus, Loris Schneider, Yitian Shi +3
In this paper, we address the point cloud registration problem, where well-known methods like ICP fail under uncertainty arising from sensor noise, pose-estimation errors, and part…
Conditional Latent Diffusion Models for Zero-Shot Instance Segmentation
Maximilian Ulmer, Wout Boerdijk, Rudolph Triebel +1
This paper presents OC-DiT, a novel class of diffusion models designed for object-centric prediction, and applies it to zero-shot instance segmentation. We propose a conditional la…
CLEVER: Stream-based Active Learning for Robust Semantic Perception from Human Instructions
Jongseok Lee, Timo Birr, Rudolph Triebel +1
We propose CLEVER, an active learning system for robust semantic perception with Deep Neural Networks (DNNs). For data arriving in streams, our system seeks human support when enco…
Towards Explaining Uncertainty Estimates in Point Cloud Registration
Ziyuan Qin, Jongseok Lee, Rudolph Triebel
Iterative Closest Point (ICP) is a commonly used algorithm to estimate transformation between two point clouds. The key idea of this work is to leverage recent advances in explaina…
How Important are Data Augmentations to Close the Domain Gap for Object Detection in Orbit?
Maximilian Ulmer, Leonard Klüpfel, Maximilian Durner +1
We investigate the efficacy of data augmentations to close the domain gap in spaceborne computer vision, crucial for autonomous operations like on-orbit servicing. As the use of co…
A Learning-based Controller for Multi-Contact Grasps on Unknown Objects with a Dexterous Hand
Dominik Winkelbauer, Rudolph Triebel, Berthold Bäuml
Existing grasp controllers usually either only support finger-tip grasps or need explicit configuration of the inner forces. We propose a novel grasp controller that supports arbit…
FFHFlow: Diverse and Uncertainty-Aware Dexterous Grasp Generation via Flow Variational Inference
Qian Feng, Jianxiang Feng, Zhaopeng Chen +2
Synthesizing diverse, uncertainty-aware grasps for multi-fingered hands from partial observations remains a critical challenge in robot learning. Prior generative methods struggle…
Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning
Jianxiang Feng, Jongseok Lee, Simon Geisler +2
To facilitate reliable deployments of autonomous robots in the real world, Out-of-Distribution (OOD) detection capabilities are often required. A powerful approach for OOD detectio…
Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly
Jianxiang Feng, Matan Atad, Ismael Rodríguez +3
Machine Learning (ML) models in Robotic Assembly Sequence Planning (RASP) need to be introspective on the predicted solutions, i.e. whether they are feasible or not, to circumvent…
Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks
Dominik Schnaus, Jongseok Lee, Daniel Cremers +1
In this work, we propose a novel prior learning method for advancing generalization and uncertainty estimation in deep neural networks. The key idea is to exploit scalable and stru…
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,…
Seeking Visual Discomfort: Curiosity-driven Representations for Reinforcement Learning
Elie Aljalbout, Maximilian Ulmer, Rudolph Triebel
Vision-based reinforcement learning (RL) is a promising approach to solve control tasks involving images as the main observation. State-of-the-art RL algorithms still struggle in t…
Making Curiosity Explicit in Vision-based RL
Elie Aljalbout, Maximilian Ulmer, Rudolph Triebel
Vision-based reinforcement learning (RL) is a promising technique to solve control tasks involving images as the main observation. State-of-the-art RL algorithms still struggle in…
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 Active Learning for Sim-to-Real Robotic Perception
Jianxiang Feng, Jongseok Lee, Maximilian Durner +1
While learning from synthetic training data has recently gained an increased attention, in real-world robotic applications, there are still performance deficiencies due to the so-c…
A Survey of Uncertainty in Deep Neural Networks
Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali +11
Due to their increasing spread, confidence in neural network predictions became more and more important. However, basic neural networks do not deliver certainty estimates or suffer…