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This course will study key algorithms relevant to programming intelligent robots with a focus on the following questions: How might one program a robot to estimate the state of the world based on multiple sources of information? How might a robot create plans or control policies for performing tasks? How might a robot learn a control policy directly from experience? The course will cover topics in estimation, control, and planning with applications to robotics including: reinforcement learning, linear optimal control, randomized motion planning, trajectory optimization, Kalman filtering, particle filtering, and selected topics in optimization.
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