With over a decade of dedicated research experience, Dr. Shahram Eivazi is leading the Festo Autonomous Systems lab at Tübingen University. He received his PhD from Finland in 2016 on the topic of a hands-free neurosurgical microscope. Then he joined Tübingen University as a postdoctoral researcher for two years. In 2019, Dr. Eivazi joined FESTO company to continue his research in the industry with a focus on robotic AI

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Currently, my research is centered on Reinforcement Learning and its practical applications within the field of robotics. I delve into novel techniques for optimizing the learning process, with a specific focus on advancing offline reinforcement learning methods

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My area of expertise lies in reinforcement learning, specifically emphasizing curriculum learning and model-based strategies. My primary mission is the efficient development of robust, adaptable agents that operate effectively with limited resources. This entails utilizing a sparse domain knowledge base and minimal environmental interactions for agent training. In the long term, my aim is to contribute significantly to the field of reinforcement learning, enabling cost-effective training techniques that can revolutionize the development of intelligent systems.

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My present research lies at the intersection of robotics and artificial intelligence, where I am dedicated to pushing the boundaries of the field and gaining fresh insights into autonomous robotic manipulation. I place particular emphasis on pioneering experiments involving the Franka Emika Panda robotic arm.

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I research under the supervision of Dr. Shahram Eivazi and Prof. Dr. Georg Martius. My research is centered on cross-domain transfer learning with a particular focus on (human) skill acquisition. The core of my work involves utilizing large language models to enhance the learning and perception abilities of robots, aiming to improve their functionality and adaptability in various tasks.

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