Kay Hansel

Kay Hansel joined the Intelligent Autonomous Systems (IAS) Lab as a Ph.D. student in May 2021. He is interested in experimenting and researching with robots, exploring new concepts in artificial intelligence such as machine learning, reinforcement learning or deep learning, and thereby discovering interdisciplinary connections between various academic disciplines.

Before starting his Ph.D., Kay Hansel completed his Bachelor's degree in Applied Mathematics at the RheinMain University of Applied Sciences and his Master's degree in Autonomous Systems at the TU Darmstadt. His thesis, "Probabilistic Dynamic Mode Primitives", was written under the supervision of Svenja Stark and Hany Abdulsamad.

Currently, Kay's research focuses on the broad field of teleoperation and shared control. In this field, an operator remotely controls a robot in potentially hazardous and unpredictable environments. Executing complex robot manipulation tasks safely under partial observability poses a real challenge to the operator. Different embodiments of robots and operators and the risk of delays make precise manipulation nearly infeasible. Therefore, Kay is currently working on shared control, where information, e.g. prior knowledge and inductive biases, are employed to improve the teleoperation.

During his academic research, Kay had the invaluable opportunity to visit the Intelligent Robotics and Biomechatronics Laboratory led by Prof. Hasegawa as a visiting scholar from May 2023 to July 2023. Part of the Department of Micro-Nano Mechanical Science and Engineering at Nagoya University, the lab is renowned for its fundamental and applied studies on mechatronics technologies for advanced human assistance systems.

Recent Publications

Robot Control

1. •       Bib
     Hansel, K.; Urain, J.; Peters, J.; Chalvatzaki, G. (2023). Hierarchical Policy Blending as Inference for Reactive Robot Control, 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE.
2. •       Bib
     Le, A. T.; Hansel, K.; Peters, J.; Chalvatzaki, G. (2023). Hierarchical Policy Blending As Optimal Transport, 5th Annual Learning for Dynamics & Control Conference (L4DC), PMLR.

Telerobotics

1. •     Bib
     Zhu, Y.; Nazirjonov, S.; Jiang, B.; Colan, J.; Aoyama, T.; Hasegawa, Y.; Belousov, B.; Hansel, K.; Peters, J. (2023). Visual Tactile Sensor Based Force Estimation for Position-Force Teleoperation, IEEE International Conference on Cyborg and Bionic Systems (CBS), pp.49-52.
   --- Best Paper Award Finalist ---
2. •     Bib
     Chen, Q.; Zhu, Y.; Hansel, Kay.; Aoyama, T.; Hasegawa, Y. (2023). Human Preferences and Robot Constraints Aware Shared Control for Smooth Follower Motion Execution, IEEE International Symposium on Micro-NanoMechatronics and Human Science (MHS), IEEE.
   --- Best Paper Award ---
3. •       Bib
     Becker, N.; Gattung, E.; Hansel, K.; Schneider, T.; Zhu, Y.; Hasegawa, Y.; Peters, J. (2024). Integrating Visuo-tactile Sensing with Haptic Feedback for Teleoperated Robot Manipulation, IEEE ICRA 2024 Workshop on Robot Embodiment through Visuo-Tactile Perception.

Robot Learning

1. •       Bib
     Hansel, K.; Moos, J.; Abdulsamad, H.; Stark, S.; Clever, D.; Peters, J. (2022). Robust Reinforcement Learning: A Review of Foundations and Recent Advances, Machine Learning and Knowledge Extraction (MAKE), 4, 1, pp.276--315, MDPI.
2. •     Bib
     Hansel, K.; Moos, J.; Derstroff, C. (2021). Benchmarking the Natural Gradient in Policy Gradient Methods and Evolution Strategies, Reinforcement Learning Algorithms: Analysis and Applications, pp.69--84, Springer.
3. •     Bib
     Belousov, B.; Abdulsamad H.; Klink, P.; Parisi, S.; Peters, J. (2021). Reinforcement Learning Algorithms: Analysis and Applications, Studies in Computational Intelligence, Springer International Publishing.
4. •     Bib
     Hansel, K. (2021). Probabilistic Dynamic Mode Primitives, Master Thesis.