Jakub Pawlak studied computer science at TU Kaiserslautern from 2014 to 2022 with a focus on robotics and artificial intelligence. His master's thesis entitled "Domain Adaptation from Synthetic Images to Real-World Scenario Using Generative Methods" dealt with the effective use of synthetic data in the context of Machine Learning.
Since 2022 he is Ph. D. candidate at the Chair of Robot Systems at TU and continues this research interest using generative deep learning methods like GANs and style transfer for robust perception in robotic systems. This includes efficient data generation and transfer of knowledge from the simulation into the real world.
- Domain Adaptation From Synthetic Images to Real-World Scenario Using Generative Methods.
- Paralinguistic Cues in Speech to Adapt Robot Behavior in Human-Robot Interaction.
Proceedings of the 9th IEEE RAS/EMBS International Conference on Biomedical Robotics & Biomechatronics (BioRob), (2022)
- Simulation Platform for Crane Visibility Safety Assistance.
Advances in Service and Industrial Robotics, Vol. 84, (2020)