Hannes Eschmann
Papers
10
Total Citations
64
H-Index
5
About
Hannes Eschmann is a robotics and control systems researcher whose work sits at the intersection of machine learning, data-driven modeling, and model predictive control (MPC). His research focuses primarily on autonomous mobile robots, Gaussian process regression, and Koopman operator theory, with a particular emphasis on developing intelligent control strategies that bridge data-driven and physics-based approaches. Eschmann's most influential contribution — his 2021 work on trajectory tracking of omnidirectional mobile robots using Gaussian process regression — has garnered 17 citations, establishing him as a notable voice in learning-augmented robot control. Alongside related work on data-based robot modeling (10 citations) and exploration-exploitation trajectory tracking via MPC (12 citations), he has built a coherent research program around using probabilistic machine learning to enhance robot autonomy and precision. A recurring theme in his recent work is the principled integration of geometric structure into data-driven control, exemplified by his provocatively titled contribution, "Data Does Not Replace Geometry," which challenges purely data-centric approaches to nonholonomic robot control using Koopman-based surrogate models. His work also extends to UAV object grasping and multi-agent systems, reflecting a broad application scope. With over 60 cumulative citations, Eschmann is an emerging researcher making meaningful contributions to the future of intelligent robotic control.
Research Focus
Key Achievements
Top Papers
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- 3Data-Based Model of an Omnidirectional Mobile Robot Using Gaussian Processes10 citations · 2021
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- 7On Koopman-based surrogate models for non-holonomic robots3 citations · 2024
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