Publications

2025

Interpretable Affordance Detection on 3D Point Clouds with Probabilistic Prototypes Maximilian Xiling Li, Korbinian Rudolf, Nils Blank, Rudolf Lioutikov arXiv preprint arXiv:2504.18355, 2025
arXiv
Use the Force, Bot! — Force-aware ProDMP with Event-based Replanning Paul Werner Lödige, Maximilian Xiling Li, Rudolf Lioutikov ICRAIEEE International Conference on Robotics and Automation, 2025
Project arXiv Code
Multi-Objective Photoreal Simulation (MOPS) Dataset for Computer Vision in Robotic Manipulation Maximilian Xiling Li, Paul Mattes, Nils Blank, Korbinian Franz Rudolf, Paul Werner Lödige, Rudolf Lioutikov Workshop on Structured World Models for Robotic Manipulation, 2025
Project

2024

An Overview of Prototype Formulations for Interpretable Deep Learning Maximilian Xiling Li, Korbinian Franz Rudolf, Paul Mattes, Nils Blank, Rudolf Lioutikov arXiv preprint arXiv:2410.08925, 2024
arXiv

2023

Information Maximizing Curriculum: A Curriculum-based Approach for Learning Versatile Skills Denis Blessing, Onur Celik, Xiaogang Jia, Moritz Reuss, Maximilian Li, Rudolf Lioutikov, Gerhard Neumann NeurIPSAdvances in Neural Information Processing Systems, 2023
Paper arXiv
Goal Conditioned Imitation Learning using Score-based Diffusion Policies Moritz Reuss, Maximilian Li, Xiaogang Jia, Rudolf Lioutikov RSSRobotics: Science and Systems, 2023
Project arXiv Code
Curriculum-based Imitation of Versatile Skills Maximilian Xiling Li, Onur Celik, Philipp Becker, Denis Blessing, Rudolf Lioutikov, Gerhard Neumann ICRAIEEE International Conference on Robotics and Automation, 2023
Paper arXiv Code
What Disrupts Flow in Office Work? The Impact of Frequency and Relevance of IT-mediated Interruptions Mario Nadj, Raphael Rissler, Marc T. P. Adam, Michael T. Knierim, Maximilian X. Li, Alexander Maedche, René Riedl MISQMIS Quarterly, 47(4), 2023
Paper

2022

Towards a Physiological Computing Infrastructure for Researching Students' Flow in Remote Learning: Preliminary Results from a Field Study Maximilian Xiling Li, Mario Nadj, Alexander Maedche, Dirk Ifenthaler, Johannes Wöhler Technology, Knowledge and Learning, 27(2), 2022
Paper

2020

To Be or Not to Be in Flow at Work: Physiological Classification of Flow using Machine Learning Raphael Rissler, Mario Nadj, Maximilian X. Li, Nico Loewe, Michael T. Knierim, Alexander Maedche IEEE Transactions on Affective Computing, 14(1), 2020
Paper
Power to the Oracle? Design Principles for Interactive Labeling Systems in Machine Learning Mario Nadj, Merlin Knaeble, Maximilian Xiling Li, Alexander Maedche KI – Künstliche Intelligenz, 34(2), 2020
Paper

2019

Flow in Knowledge Work Groups — Autonomy as a Driver or Digitally Mediated Communication as a Limiting Factor? Michael Knierim, Mario Nadj, Maximilian Li, Christof Weinhardt ICISInternational Conference on Information Systems, 2019
Paper

2018

Got Flow? Using Machine Learning on Physiological Data to Classify Flow Raphael Rissler, Mario Nadj, Maximilian Xiling Li, Michael Thomas Knierim, Alexander Maedche CHICHI Conference on Human Factors in Computing Systems (Extended Abstracts), 2018
Paper