Project description
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.
Funding and compensation
Compensation not specified. See the original posting for amounts, duration and conditions.
Eligibility and application requirements
Review the qualifications, research interests and required documents in the description above. The employer or university's original posting is the source for complete eligibility requirements.
How to apply
Submit your application through Fraunhofer's official application process before 31 Dec 2026.
Source and listing information
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