Master Thesis, 30 HP: RL-Accelerated Planning with LLM-Guided Reward Optimization

Are you a student eager to apply your theoretical knowledge and fresh perspectives to real-world challenges? At Saab, we believe that innovation thrives on new ideas, and your master thesis project could be the spark that ignites our next technological breakthrough.

Your role

We recognize the immense value that students bring to our company. Your academic rigor, combined with your enthusiasm for cutting-edge technology, allows you to approach problems with a unique and insightful lens. At Saab, you'll have the opportunity to collaborate with experienced engineers and specialists, gaining invaluable practical experience while making a tangible contribution to our growth and development.

Background

The purpose of this thesis project is to investigate how reinforcement learning (RL) can be used to accelerate classical motion planning methods for unmanned vehicles, and how large language models (LLMs) can support the development of such RL solutions through automated reward function design and refinement.

A common weakness of classical optimal motion planning algorithms is that they may require substantial computation time to converge to a feasible and optimal solution. Learning-based alternatives, which predict motion through forward passes of a neural network, can generate solutions quickly but generally provide no guarantees of convergence or optimality. Combining classical and learning-based approaches to planning is therefore a promising research direction, offering the potential to combine the reliability of optimization-based methods with the efficiency of learned models.

Reinforcement learning has demonstrated promising results in domains that can be accurately modeled in simulation. However, designing reward functions that lead to effective learning remains a challenging and often iterative process. Integrating an LLM-driven component into the reward design loop, capable of proposing and refining reward formulations based on training outcomes, may reduce the effort associated with reward engineering while improving policy performance and training efficiency. Such an approach could allow reward functions to be adapted automatically based on feedback from training runs, reducing the need for manual tuning.

Description of the Thesis Project

The work will focus on evaluating how the computational efficiency and convergence characteristics of motion planning for unmanned vehicles can be improved through learning-based components, and how the development of RL policies can be enhanced through automated reward function refinement using LLMs.

The thesis will include a literature review, the development of a simulation environment for unmanned vehicle motion planning, implementation of an RL-based planning framework, and the integration of an LLM-driven reward optimization loop. Experiments will evaluate both motion planning performance and the effectiveness of automated reward function refinement.

Your profile

The thesis will be carried out together with the Mission Autonomy group at Saab Dynamics, which works on agentic intelligence and advanced autonomy solutions for future unmanned systems.

We provide the support and guidance you need to translate your theoretical knowledge into practical solutions. Join us and become a driving force behind Saab's technological advancements!

This position requires that you pass a security vetting based on the current regulations around/of security protection. For positions requiring security clearance additional obligations on citizenship may apply.

Kindly observe that this is an ongoing recruitment process and that the position might be filled before the closing date of the advertisement.

What you will be a part of

Explore a wealth of possibilities. Take on challenges, create smart inventions, and grow beyond. This is a place for curious minds, brave pioneers, and everyone in between. Together, we achieve the extraordinary, each bringing our unique perspectives. Your part matters.

Saab is a leading defense and security company with an enduring purpose, to help nations keep their people and society safe. Empowered by its 28,000 talented people, Saab constantly pushes the boundaries of technology to create a safer and more sustainable world.

Saab designs, manufactures and maintains advanced systems in aeronautics, weapons, command and control, sensors and underwater systems. Saab is headquartered in Sweden. It has major operations all over the world and is part of the domestic defense capability of several nations. Read more about us here.