Robotics is experiencing a period of explosive growth in academia and industry. As robots are assigned more and more complex tasks to be performed in a variety of situations, it becomes essential being able to pursue multiple objectives at once while coping with uncertainty and possible failures. In this talk I will present some of our recent results in risk-aware multi objective planning leveraging the theory of constrained Markov Decision Processes. I will show how this approach can be used to tackle a variety of problems, how to manage large state spaces, and how to close the loop between theory and applications.
Speaker: Stefano Carpin, UC Merced
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