Also look at the attached PDF.
We are seeking a highly motivated student to develop a novel framework combining Reinforcement Learning (RL) with Physics-Informed Neural Networks (PINNs) that improves the reliability of RL algorithms. It can potentially increase stability of RL because PINNs enforces the physics of the system in predictions that can reduce generation of infeasible trajectories.
Basics of Physics-Informed Neural Networks (PINNs):
PINNs are a powerful machine learning technique that combines the strengths of neural networks and physics. Here's a breakdown:
Project Focus:
This project builds upon the foundation of RL and PINNs and aims to develop the control system that can model:
Furthermore, it would be part of the project to:
This thesis will, therefore, focus on the combination of data-driven ML model and Physics behind the Robotic Arms to gain the benefits of both worlds and improve the reliability of the RL predictions. Furthermore, it would be part of the project to evaluate if we can enforce physical constraints to increase safety of cobots using PINNs+RL.
Background in machine learning and reinforcement learning. Basic understanding of physics behind Multi Link Manipulators. Experience with ML libraries (e.g., Python, TensorFlow, PyTorch). Basics in Simulink would be beneficial. Basics of English is required as thesis would be in English.