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For decades, robots have been essential allies alongside humans in controlled industrial environments like heavy manufacturing facilities. However, without the guidance of a trusted human operator to shepherd a robot safely through a wide range of conditions, they have been barred from the complex, ever changing environments that we live in from day to day. Safe learning control has emerged as a promising...
Methods from machine learning have successfully been used to improve the performance of control systems in cases when accurate models of the system or the environment are not available. These methods require the use of data generated from physical trials. Transfer Learning (TL) allows for this data to come from a different, similar system. This paper studies a simplified TL scenario with the goal...
This paper introduces a learning-based robust control algorithm that provides robust stability and performance guarantees during learning. The approach uses Gaussian process (GP) regression based on data gathered during operation to update an initial model of the system and to gradually decrease the uncertainty related to this model. Embedding this data-based update scheme in a robust control framework...
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