Building things that perceive, act and learn.

Results of my master thesis presented at the ICRA 2026 Workshop on Frontiers of Optimization for Robotics in Vienna.

Open-source, Python-first robotics framework that provides a Gym-style interface for collecting data, training policies, and switching seamlessly between simulation and real-robot deployment. It includes reusable modules for control, SLAM, motion planning and visualization, and integrates natively with ROS to accelerate end-to-end robotics research.

Open-source, low-cost bimanual half-humanoid robot designed for advanced manipulation research. It features a modular, repairable hardware design and supports coordinated two-handed tasks such as folding, cutting, and assembling. The platform enables teleoperation via a Meta Quest 3 and provides interfaces for learning-based controllers to support scalable, real-world robotics experimentation.

Yeah, yeah, I'm working on it ...

Designed a real-to-sim-to-real pipeline coupling GPU-accelerated sampling-based MPC with MuJoCo MJX and a Franka Research 3 for contact-rich manipulation on the Push-T task. Demonstrated robust real-time planning that reliably escapes local minima and handles mode-switching, outperforming standard baselines. Analyzed online domain randomization limits, showing contact-initiation parameters provide stronger adaptation signals than global physics in real-time contact-rich settings.

Developement of a robotic control system for a Franka Emika Panda in a dynamic simulation environment. Focused on enabling the robot to grasp and transport objects while evading randomly moving obstacles. The pipeline integrates real-time object tracking, advanced inverse kinematics with secondary objectives, and sampling-based motion planning to achieve adaptive, collision-free manipulation. Part of the "Intelligent Robotic Manipulation" course at the Technical University of Darmstadt.

Project report exploring the mathematical structures behind 3D rotations and rigid body transformations through Lie Theory. A deep dive into the underlying theory and its applications, presented in an intuitive and visual way. The project includes the implementation of a Diffusion Model on the manifold SO(3) and explores the impact of using non-Euclidean Brownian noise and geometric loss metrics. Part of a self-organized personal student project.

Personal project report exploring the connection between information theory and optimal transport through the InfoOT framework. Studied how mutual information and entropic regularization can be combined to align data distributions, supported by implementations using Sinkhorn optimization and kernel density estimation. Part of a self-organized seminar following the book "Computational Optimal Transport" by Gabriel Peyré and Marco Cuturi.

Developement of a conditional VAE trained on a combination of medical imaging datasets. Focused on learning structured latent representations conditioned on metadata such as imaging modality or pathology. Explored data harmonization, cross-domain generalization, and visual synthesis to evaluate how conditioning improves generative performance across heterogeneous medical data. Part of the "Generative Models" course at the Technical University of Darmstadt.

Implementation of a parallel solver for large-scale surface PDEs within an Isogeometric Analysis (IGA) framework. Developed a C++ implementation of the dual-primal IETI-DP algorithm based on Schur decomposition, enabling efficient parallel solutions of domain-decomposed problems. Verified the implementation on the Laplace–Beltrami operator using spherical harmonics, achieving the expected convergence behavior and strong runtime scaling.