Ph.D. Max Spahn

About

Driven by a passion for developing innovative software solutions, my career has focused on creating efficient and effective algorithms. Over the past five years in both academic and corporate research, I have developed novel methods for solving complex computational problems and implemented existing solutions for robotics applications.

Committed to the open-source community, I advocate for making research and software developments publicly accessible to foster collaboration and continuous improvement. Understanding the complexities and challenges of reliable software systems, I am enthusiastic about joining a competitive team in my next role. I aim to contribute my expertise in software engineering and robotics to develop cutting-edge solutions in fields relevant to society.

Skills

programming: python, C++, git, ROS1/ROS2, LaTeX, bash

optimization: non-linear constrained optimization for dynamic programming, optimization on Riemannian manifolds, Bayesian optimization for hyperparameter tuning

robotics: forward and inverse kinematics, impedance control for compliant trajectory generation, receding horizon motion control, collision avoidance in human-shared environments in real-time

project management: leading and contributing to collaborative research projects, supervising several undergraduate students

languages: German, English and French

Experience

2025-

Research Scientist at ABB AG

2024

Post-Doctoral Researcher @ Delft University of Technology

Education

2020-2024

Ph.D. in Robotics @ Delft University of Technology

2013-2019

RWTH Aachen, Mechanical Engineering, B.Sc. and M.Sc.

2015-2017

École Centrale Supéléc Paris, M.Sc. (Double Degree T.I.M.E.)


Publications

Journals

2025

M Spahn, S Bakker, J Alonso-Mora: “Overcoming Explicit Environment Representations with Geometric Fabrics”, IEEE Robotics and Automation Letters

2025

T Merva, S Bakker, M Spahn, D Zhao, I Virgala, J Alonso-Mora: “Globally-Guided Geometric Fabrics for Reactive Mobile Manipulation in Dynamic Environments”, IEEE Robotics and Automation Letters

2025

C Pezzato, C Salmi, E Trevisan, M Spahn, J Alonso-Mora, CH Corbato: “Sampling-based Model Predictive Control Leveraging Parallelizable Physics Simulations”, IEEE Robotics and Automation Letters

2023

M. Spahn, M. Wisse, J. Alonso-Mora: “Dynamic Optimization Fabrics for Motion Generation”, IEEE Transactions on Robotics

2022

C. Meo, G. Franzese, C. Pezzato, M. Spahn, P. Lanillos: “Adaptation Through Prediction: Multisensory Active Inference Torque Control”, IEEE Transactions on Cognitive and Developmental Systems

2020

M. Frings, N. Hosters, C. Müller, M. Spahn, C. Susen, K. Key, S. Elgeti: SplinLib: A Modern Multipurpose C++ Spline Library”, Advances in Engineering Software

Conferences

2024

M. Spahn, C. Pezzato, C. Salmi, R. Dekker, C. Wang, C. Pek, J. Kober, J. Alonso-Mora, C. Hern'andez Corbato, M. Wisse: “Demonstrating Adaptive Mobile Manipulation in Retail Environments”, Robotics: Science and Systems

2023

M. Spahn, J. Alonso-Mora: “Autotuning Symbolic Optimization Fabrics for Trajectory Generation”, IEEE International Conference on Robotics and Automation

2023

S. Bakker, L. Knödler, M. Spahn, W. Böhmer, J. Alonso-Mora: “Multi-Robot Local Motion Planning Using Dynamic Optimization Fabrics”, IEEE International Symposium on Multi-Robot & Multi-Agent Systems

2022

M. Spahn, C. Salmi, J. Alonso-Mora: “Local Planner Bench: Benchmarking for Local Motion Planning”, Workshop at IEEE/RSJ International Conference on Intelligent Robots and Systems

2021

M. Spahn, B. Brito, J. Alonso-Mora: “Coupled Mobile Manipulation via Trajectory Optimization with Free Space Decomposition”, IEEE International Conference on Robotics and Automation

Projects

Geometric Fabrics for robot trajectory generation

Generic robotics gymnasium wrapper for pybullet and MuJoCo

Robothon 2023, Platonics Delft

Interests