City:  Stuttgart
Date:  Dec 9, 2025

Master Thesis - Reinforcement Learning for wheeled, bipedal robots

The Fraunhofer-Gesellschaft (www.fraunhofer.com) is one of the world's leading organizations for application-oriented research. 75 institutes develop pioneering technologies for our economy and society – more precisely: 32 000 people from technology, science, administration and IT. They know: Anyone who comes to Fraunhofer wants to and can make a difference. For themselves, for us and for the markets of today and tomorrow.

Advertisement for the field of study such as: automation technology, electrical engineering, computer science, cybernetics, aerospace engineering, mechanical engineering, mathematics, mechatronics, physics, control engineering, software design, software engineering, technical computer science or comparable.

 

In the Professional Service Robots - Outdoor research group we develop autonomous, mobile robots for a variety of outdoor applications, such as agriculture, forestry and logistics. The focus is on the development of an autonomous outdoor navigation solution as well as the hardware of the robots.

 

Wheeled, bipedal robots combine the advantages of dynamic walking with efficient wheeled locomotion. Controlling such systems in real-world environments is challenging due to the high-dimensional dynamics, non-linear contact interactions, and varying surface conditions. Reinforcement learning (RL) offers a promising approach to develop adaptive and robust control policies, but training on physical hardware is often impractical and unsafe. Realistic simulation environments are therefore essential. NVIDIA Isaac Sim with Isaac Lab enables high-fidelity physics simulation, sensor emulation, and RL-compatible environments for training and evaluating complex locomotion and navigation behaviours.

 

What you will do

In this thesis, you will design and implement a simulation environment for a wheeled, bipedal robot in NVIDIA Isaac Sim, ensuring realistic physics for hybrid locomotion. You will develop and train RL algorithms for hybrid locomotion tasks, including transitioning between locomotion modes and balancing on uneven terrain. To assess the quality and limitations of the training, you will compare the simulated behaviour with the real-world performance of our internally developed bipedal robot.

 

What you bring to the table

  • Valid enrollment at a German university/Hochschule
  • Background in Computer Science, Software Engineering, Mechanical Engineering, Mechatronics or similar 
  • Experience with Reinforcement Learning
  • Experience with Physic Engines is a plus
  • Experience with NVIDIA Isaac Sim and Isaac Lab is a plus
  • Experience with ROS is a plus
  • Analytical mindset
  • Enthusiasm for mobile robotics
  • Fluent in English or German

 

What you can expect

  • Cutting-edge technology in the field of outdoor mobile robotics
  • Hands on with our robots in Stuttgart
  • Take on responsibility and freedom to implement your own ideas
  • Work with the best students in their discipline
  • Familiar atmosphere including Cake Thursday

 

We value and promote the diversity of our employees' skills and therefore welcome all applications - regardless of age, gender, nationality, ethnic and social origin, religion, ideology, disability, sexual orientation and identity. Severely disabled persons are given preference in the event of equal suitability.

With its focus on developing key technologies that are vital for the future and enabling the commercial utilization of this work by business and industry, Fraunhofer plays a central role in the innovation process. As a pioneer and catalyst for groundbreaking developments and scientific excellence, Fraunhofer helps shape society now and in the future. 

Interested? Apply online now. We look forward to getting to know you!
 

Ms. Jennifer Leppich

Recruiting

+49 711 970-1415

jennifer.leppich@ipa.fraunhofer.de 

Fraunhofer Institute for Manufacturing Engineering and Automation IPA 

www.ipa.fraunhofer.de 

 

Requisition Number: 82451                Application Deadline:

 


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