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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.
Register for this course and see more details by visiting:
OpenCourser.com/course/f1cbwg/robotics
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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.
Read more
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.
Read more
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.
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.
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.
Good to know
Good to know
Know what's good ,
what to watch for , 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
Generally positive for learners
Learners say this is a generally positive and well received course on robotics. One of the learners said they: "good course ..expecting more follow up courses on this topic !"
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
Show steps
Refresh linear algebra skills to enhance understanding of probabilistic models.
Browse courses on
Linear Algebra
Show steps
-
Review basic concepts of linear algebra, such as vectors, matrices, and transformations.
Probabilistic Robotics
Show steps
Review a classic text to broaden understanding of probabilistic robotics.
View
Probabilistic Robotics (Intelligent Robotics...
on Amazon
Show steps
-
Read selected chapters from Probabilistic Robotics.
-
Summarize key concepts and algorithms.
Connect with robotics researchers
Show steps
Seek guidance from experts to enhance learning and gain insights.
Show steps
-
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
Show steps
Supplement understanding of Gaussian Process Regression for probabilistic inference.
Show steps
-
Find and follow a tutorial on Gaussian Process Regression.
-
Implement and apply Gaussian Process Regression for a practical problem.
Occupancy grid mapping project
Show steps
Create an occupancy grid map to simulate real-world mapping scenarios.
Show steps
-
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
Show steps
Enhance understanding of robot localization and mapping algorithms.
Show steps
-
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
Show steps
Practice dynamic system learning to enhance understanding.
Show steps
-
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
Show steps
Build a virtual environment for 3D mapping and navigation.
Show steps
-
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.
Review linear algebra fundamentals
Show steps
Refresh linear algebra skills to enhance understanding of probabilistic models.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review basic concepts of linear algebra, such as vectors, matrices, and transformations.
Probabilistic Robotics
Show steps
Review a classic text to broaden understanding of probabilistic robotics.
View
Probabilistic Robotics (Intelligent Robotics...
on Amazon
Show steps
Show steps
Show steps
- Read selected chapters from Probabilistic Robotics.
- Summarize key concepts and algorithms.
Connect with robotics researchers
Show steps
Seek guidance from experts to enhance learning and gain insights.
Show steps
Show steps
Show steps
- 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
Show steps
Supplement understanding of Gaussian Process Regression for probabilistic inference.
Show steps
Show steps
Show steps
- Find and follow a tutorial on Gaussian Process Regression.
- Implement and apply Gaussian Process Regression for a practical problem.
Occupancy grid mapping project
Show steps
Create an occupancy grid map to simulate real-world mapping scenarios.
Show steps
Show steps
Show steps
- 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
Show steps
Enhance understanding of robot localization and mapping algorithms.
Show steps
Show steps
Show steps
- 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
Show steps
Practice dynamic system learning to enhance understanding.
Show steps
Show steps
Show steps
- 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
Show steps
Build a virtual environment for 3D mapping and navigation.
Show steps
Show steps
Show steps
- 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 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.
Software 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.
Data Scientist
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.
Mechanical Engineer
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.
Civil 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.
Nuclear 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.
Aerospace 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.
Electrical 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.
Industrial 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.
Systems 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.
Computer 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.
Materials 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.
Chemical 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.
Environmental 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.
Biomedical 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.
For more career information including salaries, visit:
OpenCourser.com/course/f1cbwg/robotics
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.
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.
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.
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.
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.
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.
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.
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.
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.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/f1cbwg/robotics
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Robotics: Mobility
Effort
4 weeks of study, 3-4 hours/week
Via
Coursera
Institution
University of Pennsylvania
Instructor
Daniel Lee
Language
English
Transcripts
Español
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Deutsch
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中文
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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
Good to know
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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