How can robots determine their state and properties of the surrounding environment from noisy sensor measurements in time? In this module you will learn how to get robots to incorporate uncertainty into estimating and learning from a dynamic and changing world. Specific topics that will be covered include probabilistic generative models, Bayesian filtering for localization and mapping.
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The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies.
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ROBOTICS: ESTIMATION AND LEARNING의 최상위 리뷰
This is a really comprehensive course which gave me a good knowledge about Gaussian Model and Kalman Filter ...
It's a great course. Although the assignment is little tough, you will gain a lot after completing it.
Week 1 and Week 3 are organized much better than Week 2 and Week 4. If you don't have enough time, I recommend that you focus on Week 1 and 3.
A tough course with few hours of lecture material and some good programming assignments.You will be satisfied by those assignments however .
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The Introduction to Robotics Specialization introduces you to the concepts of robot flight and movement, how robots perceive their environment, and how they adjust their movements to avoid obstacles, navigate difficult terrains and accomplish complex tasks such as construction and disaster recovery. You will be exposed to real world examples of how robots have been applied in disaster situations, how they have made advances in human health care and what their future capabilities will be. The courses build towards a capstone in which you will learn how to program a robot to perform a variety of movements such as flying and grasping objects.
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