3 papers
cs.RO2026
Bootstrapping Self-Supervised Learning of Binary Classification Using Error Bounds: A Case Study on a Robotic Insertion Task
Zebin Duan, Norbert Krüger, Juan Heredia +2
Flexible manufacturing requires rapid deployment of solutions and minimal setup time to remain competitive. An essential attribute is the ability to control error levels, as failur…
cs.LG2026
Estimation of Confidence Bounds in Binary Classification using Wilson Score Kernel Density Estimation
Thorbjørn Mosekjær Iversen, Zebin Duan, Frederik Hagelskjær
The performance and ease of use of deep learning-based binary classifiers have improved significantly in recent years. This has opened up the potential for automating critical insp…
cs.RO2025
Towards High Precision: An Adaptive Self-Supervised Learning Framework for Force-Based Verification
Zebin Duan, Frederik Hagelskjær, Aljaz Kramberger +2
The automation of robotic tasks requires high precision and adaptability, particularly in force-based operations such as insertions. Traditional learning-based approaches either re…