Sampling-based motion planning algorithms like Rapidly-exploring Random Trees (RRT) and Probabilistic Roadmaps (PRM) have proven to be effective in high-dimensional configuration spaces and can handle complex, real-world environments. However, this approach can suffer from the curse of dimensionality, where the number of samples needed to cover the configuration space grows exponentially with the number of dimensions.
We are seeking a motivated and talented student to join our research team as a Masterthesis Intern in robot motion planning: Leveraging statistical machine learning to increase the efficiency of motion planning through identification of the environment state. The successful candidate will have the opportunity to work on cutting-edge projects in the field of robotics and contribute to the advancement of motion planning algorithms.

Your responsibilities will be:

• conduct research in the field of sampling-based motion planning, focusing on the development and implementation of new algorithms to exploit previous experience to increase efficiency
• design and conduct experiments to evaluate the performance of the developed algorithm
• write and present a comprehensive Master's thesis on the research work

We offer:

• the opportunity to work on real manipulators for industrial automation
• an opportunity to learn from experienced robotics professionals
• a chance to grow and apply your skills and knowledge