Master Thesis Reinforcement Learning for wheeled, bipedal robots

Fraunhofer-Gesellschaft · Stuttgart

ThesisGerman needed: none

Still open on 1 Nov

You apply on the company's own website.

Key facts

Subject you study: Automation Technology, Electrical Engineering, Computer Science, Cybernetics, Aerospace Engineering, Mechanical Engineering

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.

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.

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.