Christopher Diehl

Christopher Diehl

Machine Learning and Robotics Researcher

TU Dortmund University

Hello World! I’m a researcher and doctoral candidate at TU Dortmund, Germany. My research interests include machine learning (ML), robotics, and multi-agent systems, and I develop algorithms for ML-based behavior prediction and planning. In my work, I love to combine ideas from and draw connections between different fields (reinforcement learning, optimal control, generative models, and game theory), which led to publications and presentations in venues such as NeurIPS, ICML, CoRL, RA-L/IROS. In addition, I collaborated with different partners from the German autonomous vehicle industry.

Research Interests

Reinforcement Learning
Imitation Learning
Generative Models
Optimal Control
Game Theory
Differentiable Optimization

Experience

 
 
 
 
 
Research Assistant and Doctoral Researcher
TU Dortmund University (Robotics Research Institute)
October 2019 – Present Dortmund
Research on large-scale ML-based behavior prediction and planning.
 
 
 
 
 
Research Intern
Hella Aglaia Mobile Vision (acquired by Volkswagen’s CARIAD SE)
January 2019 – July 2019 Berlin
Worked on sensor fusion, object detection, tracking, and mapping.
 
 
 
 
 
Student Researcher
TU Dortmund University
April 2017 – September 2018 Dortmund
Developed a game-theoretic motion planner (project with ZF Group).
 
 
 
 
 
Software Engineering Intern
Bertrandt AG
February 2016 – May 2016 Cologne
Implemented control strategies for autonomous driving simulation.
 
 
 
 
 
Software Engineering Intern
Hewlett-Packard GmbH
January 2011 – February 2011 Dortmund
Worked on app development for server applications.

Awards

Youth Author Award
Best Paper Award
See certificate
Master Graduation with Distinction
German Scholarschip

Recent Publications

Quickly discover relevant content by filtering publications.
(2023). Differentiable Constrained Imitation Learning for Robot Motion Planning and Control. IEEE International Conference on Intelligent Robots and Systems Agent4AD Workshop (IROS), 2023.

(2023). Social Behavior Prediction for Automated Vehicles Using Contrastive Learning. IFToMM D‑A‑CH, 2023.

(2022). Conditional Behavior Prediction for Automated Driving on Highways. Proc. 32. Workshop Computational Intelligence. 2022 Best Paper Award.

(2022). Beyond Perception: Environmental Model Completion by Reasoning for Occluded Vehicles. IEEE Robotics and Automation Letters (RA‑L)/ International Conference on Intelligent Robots and Systems (IROS), 2022.

(2022). Time‑Optimal Nonlinear Model Predictive Control for Radar‑based Automated Parking. IFAC Symposium on Intelligent Autonomous Vehicles (IAV), 2022.

Contact

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