Seamless cooperation between humans and autonomous agents is a vital yet challenging aspect of modern robotic systems. Effective cooperation between humans and autonomous systems can be achieved if the autonomous systems are capable of learning to act by observing other cognitive entities. Based on the premise that a cost (or reward) function fully characterizes the intent of the demonstrator, we develop methods to learn the cost function from observations for linear and nonlinear uncertain systems.

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rushikesh.kamalapurkar@okstate.edu
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