8 papers
Optimal Learning-Augmented Algorithm for Online Bidding
Changyeol Lee, Dahoon Lee, Jongseo Lee +2
Recent advances in machine learning have spurred significant interest in learning-augmented algorithms, particularly for online optimization. A growing body of work has studied onl…
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…
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…
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…
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,…
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…