Christian Stromberger

Balanced and simultaneously worked on several projects including software development for deployment on robots, hardware design with SOLIDWORKS, and more. Developed novel robotic perception algorithms using Python, PyTorch, C++, and ViTs which I wrote about in a second author paper accepted to MECC 2025. Asked by manager and PI to extend my internship from on-site summer to remote year-round to continue my work on robotic perception and SLAM.

Working in the robotic walk lab using machine learning techniques to create a faster and probabilistically safe robotic perception and planning framework. Created and trained a neural network using PyTorch to learn a compact forward occupancy reach set over-approximation, and a network which learns the signed distance field in an environment defined by a set of obstacles defined as zonotopes. Demonstrated findings on robotic arm inside a Gazebo simulation environment using ROS. Integrated results into the lab’s ongoing academic research and codebases.

Worked in a fast-paced environment to develop, assemble, wire, and test an all-terrain surveying robot. Designed, soldered, assembled, and internally deployed several IP65 electronic boxes with power meters to provide the company and customer with essential power usage data to prove the robot met contract requirements. Ran tests on the robot and identified critical issues, allowing them to be fixed before deployment. In charge of designing, wiring, and manufacturing all charging accessories, including LED charge level indicators and CAN communication ports. All were attached to the battery charger and shipped directly to the customer.

In charge of a project that successfully used Python, PyTorch, and ROS to design and implement a model-free algorithm which enables a system to autonomously detect failures and reconfigure the system controller to continue to make progress towards a mission goal despite being in a reduced capacity state. First author paper titled A Model-Free Algorithm for Extremely Resilient Navigation submitted to ICRA 2023.

Designed and produced a mechanical fish fin which changed shape while swimming under many strict design constraints. Used the fin to successfully quantify the effects of length to height ratio on forces produced by the fin, with applications to autonomous underwater vehicles (AUVs).

Mechanical Engineering Major Computer Science Minor

Pursuing a MS Robotics Degree from the University of Michigan.