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Robotics

Estimation and Learning

Daniel Lee

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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What's inside

Syllabus

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.

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Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Introduces concepts of robotic autonomation, a growing field

Develops skills in Gaussian Model Learning, fundamental for robotics

Teaches Bayesian Estimation and Target Tracking techniques

Covers Mapping and Localization in robotics

Applicable knowledge for robotics engineers and students

Requires knowledge of math and robotics concepts

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Reviews summary

Robotics estimation and learning fundamentals

According to learners, this course offers a strong foundation in probabilistic methods for robotics, focusing on topics like Bayesian filtering, mapping, and localization. Students find the theoretical content challenging yet valuable, particularly the deep dives into Kalman filters and occupancy grid mapping. While many appreciate the expert instruction and the course's relevance, some indicate that the prerequisites are substantial and the assignments can be quite difficult, requiring significant independent study. Overall, it's viewed as a rigorous and rewarding course for those with a solid mathematical and engineering background.

Covers essential topics for robotics.

"The topics covered, like SLAM and localization, are directly relevant to modern robotics."

"This course teaches fundamental concepts crucial for working in the robotics industry."

"Understanding estimation and learning is vital, and this course delivers on that."

"Mapping and localization modules were particularly useful for practical application."

Instructors demonstrate deep knowledge.

"The instructors are clearly experts in the field and explain complex topics well."

"Their passion for the subject comes through in the lectures."

"I appreciated the depth of knowledge the professors brought to the material."

"Lectures are well-delivered by knowledgeable faculty."

Provides a solid grounding in key concepts.

"This course provided a strong foundation in the theoretical underpinnings of robotic estimation."

"The lectures offer a comprehensive overview of the probabilistic methods required for SLAM."

"I gained a deep understanding of Bayesian methods and Kalman filters."

"The mathematical rigor is high, which is great for building foundational knowledge."

Material moves quickly at times.

"Sometimes the lectures move very quickly, and I had to rewatch sections multiple times."

"The amount of material covered each week felt dense."

"Keeping up with the pace requires dedicated study time."

"Wish some of the more difficult concepts were broken down more slowly."

Needs strong background in math and programming.

"You definitely need a strong background in linear algebra and probability theory to keep up."

"Prior experience with C++ and potentially ROS is highly recommended to tackle the assignments."

"The course assumes a certain level of mathematical maturity that might be tough for beginners."

"Make sure you are comfortable with matrix operations and Bayes' theorem before starting."

Homework requires significant effort and prior knowledge.

"The assignments were significantly harder than I anticipated, often requiring knowledge outside the lectures."

"Homework problems are tough; expect to spend a lot of time on them."

"Completing the coding assignments tested my limits but ultimately helped me learn."

"Some assignments felt disconnected from the lecture material or assumed advanced programming skills."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Robotics: Estimation and Learning with these activities:

Review linear algebra fundamentals

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Refresh linear algebra skills to enhance understanding of probabilistic models.

Browse courses on Linear Algebra

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Review basic concepts of linear algebra, such as vectors, matrices, and transformations.

Probabilistic Robotics

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Review a classic text to broaden understanding of probabilistic robotics.

View Probabilistic Robotics (Intelligent Robotics... on Amazon

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Read selected chapters from Probabilistic Robotics.
Summarize key concepts and algorithms.

Connect with robotics researchers

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Seek guidance from experts to enhance learning and gain insights.

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Identify and reach out to researchers in the field of robotics.
Attend webinars or conferences to connect with potential mentors.

Five other activities

Expand to see all activities and additional details

Show all eight activities

Tutorial on Gaussian Process Regression

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Supplement understanding of Gaussian Process Regression for probabilistic inference.

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Find and follow a tutorial on Gaussian Process Regression.
Implement and apply Gaussian Process Regression for a practical problem.

Occupancy grid mapping project

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Create an occupancy grid map to simulate real-world mapping scenarios.

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Design and implement an algorithm for occupancy grid mapping.
Generate a simulated environment for robot navigation.
Test and evaluate the performance of the occupancy grid map.

Localization and mapping practice problems

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Enhance understanding of robot localization and mapping algorithms.

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Solve and discuss practice problems on localization and mapping algorithms.
Implement and test different localization and mapping algorithms.
Analyze and compare the performance of different algorithms.

Dynamic model learning exercises

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Practice dynamic system learning to enhance understanding.

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Solve practice problems and examples on dynamic system modeling.
Implement algorithms for linear and non-linear dynamic system learning.
Test and evaluate learned dynamic models.

3D mapping simulation project

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Build a virtual environment for 3D mapping and navigation.

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Design and develop a 3D environment for robot simulation.
Implement 3D mapping algorithms in the simulation.
Test and evaluate the performance of the mapping algorithms.

Career center

Learners who complete Robotics: Estimation and Learning will develop knowledge and skills that may be useful to these careers:

Robotics Engineer

Robotics Engineers design, build, and maintain robots, which are used in a variety of industries including manufacturing, healthcare, and space exploration. Robotics: Estimation and Learning can help a Robotics Engineer succeed in their role by providing them with a strong foundation in probabilistic generative models, Bayesian filtering for localization and mapping, and other topics, which are essential for developing and deploying robots that can operate autonomously in complex and dynamic environments.

See salaries and explore the career path for Robotics Engineer

Software Engineer

Software Engineers design, develop, and maintain software systems. Robotics: Estimation and Learning can help a Software Engineer succeed in their role by providing them with a strong foundation in probabilistic generative models, Bayesian filtering for localization and mapping, and other topics, which are essential for developing and deploying software systems that can operate in complex and dynamic environments, such as those found in robotics.

See salaries and explore the career path for Software Engineer

Data Scientist

Data Scientists use data to solve problems and make predictions. Robotics: Estimation and Learning can help a Data Scientist succeed in their role by providing them with a strong foundation in probabilistic generative models, Bayesian filtering for localization and mapping, and other topics, which are essential for developing and deploying data-driven solutions to problems in a variety of fields, including robotics.

See salaries and explore the career path for Data Scientist

Mechanical Engineer

Mechanical Engineers design, build, and maintain mechanical systems. Robotics: Estimation and Learning can help a Mechanical Engineer succeed in their role by providing them with a strong foundation in probabilistic generative models, Bayesian filtering for localization and mapping, and other topics, which are essential for developing and deploying mechanical systems that can operate in complex and dynamic environments, such as those found in robotics.

See salaries and explore the career path for Mechanical Engineer

Electrical Engineer

Electrical Engineers design, build, and maintain electrical systems. Robotics: Estimation and Learning can help an Electrical Engineer succeed in their role by providing them with a strong foundation in probabilistic generative models, Bayesian filtering for localization and mapping, and other topics, which are essential for developing and deploying electrical systems that can operate in complex and dynamic environments, such as those found in robotics.

See salaries and explore the career path for Electrical Engineer

Systems Engineer

Systems Engineers design, build, and maintain complex systems. Robotics: Estimation and Learning can help a Systems Engineer succeed in their role by providing them with a strong foundation in probabilistic generative models, Bayesian filtering for localization and mapping, and other topics, which are essential for developing and deploying complex systems, such as those found in robotics.

See salaries and explore the career path for Systems Engineer

Computer Engineer

Computer Engineers design, build, and maintain computer systems. Robotics: Estimation and Learning can help a Computer Engineer succeed in their role by providing them with a strong foundation in probabilistic generative models, Bayesian filtering for localization and mapping, and other topics, which are essential for developing and deploying computer systems that can operate in complex and dynamic environments, such as those found in robotics.

See salaries and explore the career path for Computer Engineer

Aerospace Engineer

Aerospace Engineers design, build, and maintain aircraft, spacecraft, and other aerospace vehicles. Robotics: Estimation and Learning can help an Aerospace Engineer succeed in their role by providing them with a strong foundation in probabilistic generative models, Bayesian filtering for localization and mapping, and other topics, which are essential for developing and deploying aerospace vehicles that can operate in complex and dynamic environments, such as those found in space.

See salaries and explore the career path for Aerospace Engineer

Biomedical Engineer

Biomedical Engineers design, build, and maintain medical devices and systems. Robotics: Estimation and Learning can help a Biomedical Engineer succeed in their role by providing them with a strong foundation in probabilistic generative models, Bayesian filtering for localization and mapping, and other topics, which are essential for developing and deploying medical devices and systems that can operate in complex and dynamic environments, such as those found in the human body.

See salaries and explore the career path for Biomedical Engineer

Chemical Engineer

Chemical Engineers design, build, and maintain chemical plants and processes. Robotics: Estimation and Learning can help a Chemical Engineer succeed in their role by providing them with a strong foundation in probabilistic generative models, Bayesian filtering for localization and mapping, and other topics, which are essential for developing and deploying chemical plants and processes that can operate in complex and dynamic environments, such as those found in the manufacturing industry.

See salaries and explore the career path for Chemical Engineer

Civil Engineer

Civil Engineers design, build, and maintain infrastructure, such as roads, bridges, and buildings. Robotics: Estimation and Learning can help a Civil Engineer succeed in their role by providing them with a strong foundation in probabilistic generative models, Bayesian filtering for localization and mapping, and other topics, which are essential for developing and deploying infrastructure that can operate in complex and dynamic environments, such as those found in urban areas.

See salaries and explore the career path for Civil Engineer

Environmental Engineer

Environmental Engineers design, build, and maintain systems to protect the environment. Robotics: Estimation and Learning can help an Environmental Engineer succeed in their role by providing them with a strong foundation in probabilistic generative models, Bayesian filtering for localization and mapping, and other topics, which are essential for developing and deploying environmental systems that can operate in complex and dynamic environments, such as those found in the natural world.

See salaries and explore the career path for Environmental Engineer

Industrial Engineer

Industrial Engineers design, build, and maintain systems to improve the efficiency and productivity of industrial processes. Robotics: Estimation and Learning can help an Industrial Engineer succeed in their role by providing them with a strong foundation in probabilistic generative models, Bayesian filtering for localization and mapping, and other topics, which are essential for developing and deploying industrial systems that can operate in complex and dynamic environments, such as those found in factories.

See salaries and explore the career path for Industrial Engineer

Materials Engineer

Materials Engineers design, build, and maintain materials for a variety of applications, including aerospace, automotive, and biomedical. Robotics: Estimation and Learning can help a Materials Engineer succeed in their role by providing them with a strong foundation in probabilistic generative models, Bayesian filtering for localization and mapping, and other topics, which are essential for developing and deploying materials that can operate in complex and dynamic environments, such as those found in extreme conditions.

See salaries and explore the career path for Materials Engineer

Nuclear Engineer

Nuclear Engineers design, build, and maintain nuclear power plants and other nuclear facilities. Robotics: Estimation and Learning can help a Nuclear Engineer succeed in their role by providing them with a strong foundation in probabilistic generative models, Bayesian filtering for localization and mapping, and other topics, which are essential for developing and deploying nuclear power plants and other nuclear facilities that can operate in complex and dynamic environments, such as those found in nuclear power plants.

See salaries and explore the career path for Nuclear Engineer

Reading list

We've selected eight books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Robotics: Estimation and Learning.

Cover image

Cover image

Probabilistic Robotics

Save

Provides a comprehensive introduction to probabilistic robotics, covering topics such as state estimation, mapping, and control. It valuable resource for students and researchers in robotics.

Probabilistic Robotics (Intelligent Robotics and...

Probabilistic Robotics (Intelligent Robotics and...

Cover image

Cover image

Bayesian Filtering and Smoothing

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Provides a comprehensive treatment of Bayesian filtering and smoothing, with a focus on applications in robotics. It valuable resource for students and researchers in robotics who wish to learn more about these techniques.

Bayesian Filtering and Smoothing (Institute of...

Bayesian Filtering and Smoothing (Institute of...

Bayesian Filtering and Smoothing (Institute of...

Bayesian Filtering and Smoothing (Institute of...

Gaussian Processes for Machine Learning

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Provides a comprehensive introduction to Gaussian processes, a powerful machine learning technique that is widely used in robotics. It valuable resource for students and researchers in robotics who wish to learn more about this technique.

Gaussian Processes for Machine Learning (Adaptive...

Cover image

Cover image

Robotics, Vision and Control

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Provides a comprehensive introduction to robotics, vision, and control. It valuable resource for students and researchers in robotics who wish to learn more about these topics.

Robotics, Vision and Control: Fundamental...

Robotics, Vision and Control: Fundamental...

Cover image

Cover image

Kinematics and Dynamics of Mechanical Systems

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Provides a comprehensive introduction to robotics, covering topics such as kinematics, dynamics, and control. It valuable resource for students and researchers in robotics who wish to learn more about these topics.

Kinematics and Dynamics of Mechanical Systems

Kinematics and Dynamics of Mechanical Systems:...

Kinematics and Dynamics of Mechanical Systems,...

Robot Modeling and Control

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Provides a comprehensive introduction to robot modeling and control. It valuable resource for students and researchers in robotics who wish to learn more about these topics.

Robot Modeling and Control

Robot Modeling and Control

Cover image

Cover image

Introduction to Robotics

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Provides a comprehensive introduction to the control of robot manipulators. It valuable resource for students and researchers in robotics who wish to learn more about this topic.

Introduction to Robotics: Mechanics and Control...

Introduction to Robotics Mechanics and Control

Introduction to Robotics: Mechanics and Control...

Introduction to Robotics: Mechanics and Control by...

Cover image

Cover image

Intelligent Autonomous Robotics

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Provides a comprehensive introduction to robot learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for students and researchers in robotics who wish to learn more about these techniques.

Intelligent Autonomous Robotics

Intelligent Autonomous Robotics

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Effort

4 weeks of study, 3-4 hours/week

Via

Coursera

Institution

University of Pennsylvania

Instructor

Daniel Lee

Language

English

Transcripts

Español Français Português Deutsch Italiano 中文 العربية 한국어 日本語 हिंदी Nederlands Svenska Pусский Türkçe Polski Ελληνικά українська мова magyar nyelv Bahasa Indonesia ไทย қазақша اُردُو, فارسی বাংলা پښتو آذربایجان دیلی

This course belongs to a collection called Robotics. It's comprised of seven courses:

Robotics: Mobility
Robotics: Aerial Robotics
Robotics: Estimation and Learning (viewing)
Robotics: Capstone
Robotics: Perception
Robotics: Computational Motion Planning
Robotics: Kinematics and Mathematical Foundations

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Introduces concepts of robotic autonomation, a growing field

Develops skills in Gaussian Model Learning, fundamental for robotics

Teaches Bayesian Estimation and Target Tracking techniques

Covers Mapping and Localization in robotics

Applicable knowledge for robotics engineers and students

Requires knowledge of math and robotics concepts

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