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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Robotics SpecializationUniversity of PennsylvaniaAbout this Course
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Learner Career Outcomes
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started a new career after completing these courses
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got a tangible career benefit from this course
20%
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Course 5 of 6 in the
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Approx. 15 hours to complete
English
Subtitles: English, Spanish, Chinese (Simplified)
Skills you will gain
Particle FilterEstimationMapping
Learner Career Outcomes
40%
started a new career after completing these courses
43%
got a tangible career benefit from this course
20%
got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 5 of 6 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 15 hours to complete
English
Subtitles: English, Spanish, Chinese (Simplified)
Offered by
University of Pennsylvania
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.
Syllabus - What you will learn from this course
4 hours to complete
Gaussian Model Learning
We will learn about the Gaussian distribution for parametric modeling in robotics. The Gaussian distribution is the most widely used continuous distribution and provides a useful way to estimate uncertainty and predict in the world. We will start by discussing the one-dimensional Gaussian distribution, and then move on to the multivariate Gaussian distribution. Finally, we will extend the concept to models that use Mixtures of Gaussians.
4 hours to complete
9 videos (Total 52 min), 3 readings, 1 quiz
9 videos
WEEK 1 Introduction1m
1.2.1. 1D Gaussian Distribution8m
1.2.2. Maximum Likelihood Estimate (MLE)6m
1.3.1. Multivariate Gaussian Distribution7m
1.3.2. MLE of Multivariate Gaussian4m
1.4.1. Gaussian Mixture Model (GMM)4m
1.4.2. GMM Parameter Estimation via EM7m
1.4.3. Expectation-Maximization (EM)6m
3 readings
MATLAB Tutorial - Getting Started with MATLAB10m
Setting Up your MATLAB Environment10m
Basic Probability10m
3 hours to complete
Bayesian Estimation - Target Tracking
We will learn about the Gaussian distribution for tracking a dynamical system. We will start by discussing the dynamical systems and their impact on probability distributions. This linear Kalman filter system will be described in detail, and, in addition, non-linear filtering systems will be explored.
3 hours to complete
5 videos (Total 21 min)
5 videos
Kalman Filter Motivation4m
System and Measurement Models5m
Maximum-A-Posterior Estimation4m
Extended Kalman Filter and Unscented Kalman Filter4m
4 hours to complete
Mapping
We will learn about robotic mapping. Specifically, our goal of this week is to understand a mapping algorithm called Occupancy Grid Mapping based on range measurements. Later in the week, we introduce 3D mapping as well.
4 hours to complete
6 videos (Total 36 min)
6 videos
Introduction to Mapping7m
3.2.1. Occupancy Grid Map6m
3.2.2. Log-odd Update6m
3.2.3. Handling Range Sensor6m
Introduction to 3D Mapping8m
3 hours to complete
Bayesian Estimation - Localization
We will learn about robotic localization. Specifically, our goal of this week is to understand a how range measurements, coupled with odometer readings, can place a robot on a map. Later in the week, we introduce 3D localization as well.
3 hours to complete
6 videos (Total 23 min)
6 videos
Odometry Modeling5m
Map Registration5m
Particle Filter4m
Iterative Closest Point5m
Closing45s
Reviews
TOP REVIEWS FROM ROBOTICS: ESTIMATION AND LEARNING
by SSApr 6, 2017
Leanring of mechanism and implementation of Kalman filter and particle filter from experiment is very interesting for me. And these method let me know more about map building in SLAM framework.
by SJAug 6, 2016
Lesson 1 and Lesson 3 are clear. However, homework in Lesson 2 and Lesson 4 is hard to finish because of too few materials in the lesson. Overall, it is a fairly good course.
by VGFeb 15, 2017
The material is clearly presented. The Matlab exercises complement and reinforce the subject, the level of difficulty is well balanced, thanks for this great course.
by NNJun 19, 2016
This is course is really helpful for beginners to understand how probability is useful in Robotics.Assignments are bit tough but worth the time .
About the Robotics Specialization
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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