International Chairs
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RL4Robots
Objectives of the chair Robotics has seen tremendous progress in recent years thanks to advances in simulators, optimizers, and reinforcement learning. Despite impressive demonstrations, however, these methods have yet to be fully deployed in real-world settings, and scaling beyond the lab remains a challenge. What if we could harness the full potential of constrained optimization
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Hailsed
Objectives of the chair Recently, scientific machine learning (SciML) has expanded the capabilities of traditional numerical approaches, by simplifying computational modeling and providing cost-effective surrogates. However, SciML surrogates suffer from absence of the explicit error control, computationally intensive training phase, and the lack of reliability in practice. HAILSED chair aims to tackle these challenges by