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including, the curse of dimensionality and how it impacts control problems (including how some systems can be decomposed into simpler control problems), how simulation can be leveraged before trying learning on a physical robot, safe sets, and how a robot can modify its behavior based on how confident it is that its model is correct.

He works to enable AI systems to reason explicitly about the gap between their models and the real world, so that they can safely interact with uncertain environments and human beings, even under inaccurate assumptions.

#280: Semantics In Robotics, With Amy Loutfi Robohub podcast

In this episode, Audrow Nash interviews Amy Loutfi, a professor at Örebro University, about how semantic representations can be used to help robots reason about the world.