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Course Report

21 Free Robotics Courses to Help Clear the Path to Singularity

From the self driving cars to humanoid robots, the future of artificial intelligence is here.

In 2015 the topic of robotics and artificial intelligence was brought to center stage and became a household concept, and concern. We saw the philosophical and moral conundrums regarding AI raised in the movie “Ex Machina.” Spectacular feats were performed by Boston Dynamics’ “Spot” the dog and “Atlas” the maid while Elon Musk dumped millions into a program to save us from ourselves. It wasn’t just the arrival of sentient AI but the coming of age for the autonomous vehicle. Business Insider reported that 10 million self driving cars will be on the road by 2020, with Musk making loftier claims that a fully automated Tesla will be available in 2 years. As Uber and Lyft invest heavily in engineering talent in their race for the empty driver’s seat, we investigate 26 courses to get you up to speed.

Introduction to Robotics

Interacting with Baxter the robot from ‘Begin Robotics’ FutureLearn class

Begin Robotics
University of Reading via FutureLearn
Explore the history, anatomy and intelligence of robots with this free online course. Test drive robots using exciting simulations
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Mobile Robotics
via Open2Study
Discover the world of mobile robots – how they move, how they interact with the world, and how to build them!
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Introduction to Robotics
Queensland University of Technology via EdCast
Intro to the exciting world of robotics and the mathematics and algorithms that underpin it.
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QUT MOOC Introduction to Robotics Trailer

AMRx: Autonomous Mobile Robots
ETH Zurich via edX
Robots are rapidly evolving from factory workhorses, which are physically bound to their work-cells, to increasingly complex machines capable of performing challenging tasks in our daily environment. The objective of this course is to provide the basic concepts and algorithms required to develop mobile robots that act autonomously in complex environments.
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Drones & Aerial Robotics

Unmanned copter flight

Robotics: Aerial Robotics
University of Pennsylvania via Coursera
Gain an intro to the mechanics of flight and the design of quadrotor flying robots.
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Unmanned Aerospace Systems (UAS) – Key Concepts for New Users
Embry-Riddle Aeronautical University via
This two-part course covers key concepts related to unmanned aerospace systems (UAS)—also known as recreational drones—including basic types or groups, capabilities, and current and future uses. Particular emphasis is placed on safety of flight within the National Airspace System (NAS), including where to find online flight planning tools to help make every flight as safe as possible.
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AUTONAVx: Autonomous Navigation for Flying Robots
Technische Universität München (Technical University of Munich) via edX
In this course, we will introduce the basic concepts for autonomous navigation for quadrotors. The following topics will be covered: 3D geometry, probabilistic state estimation, visual odometry, SLAM, 3D mapping, linear control. In particular, you will learn how to infer the position of the quadrotor from its sensor readings and how to navigate it along a trajectory.
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Movement, Sensors & Actuation

By brett jordan (Roboscribe) [CC BY 2.0], via Wikimedia Commons

Robotics: Mobility
University of Pennsylvania via Coursera
How can robots use their motors and sensors to move around in an unstructured environment? You will understand how to design robot bodies and behaviors that recruit limbs and more general appendages to apply physical forces that confer reliable mobility in a complex and dynamic world.
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Robotics: Computational Motion Planning
University of Pennsylvania via Coursera
Robotic systems typically include three components: a mechanism which is capable of exerting forces and torques on the environment, a perception system for sensing the world and a decision and control system which modulates the robot’s behavior to achieve the desired ends. You will learn some of the most common approaches to addressing this problem including graph-based methods, potential fields, randomized planners and optimization-based methods.
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6.302.0x: Introduction to Feedback Control Theory
Massachusetts Institute of Technology via edX
Have you wondered about the design strategies behind temperature controllers, quad-copters, or self-balancing scooters? Are you interested in robotics, and have heard of, or tried, “line-following” or “PID control” and want to understand more?
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Internet of Things: Sensing and Actuation From Devices
University of California, San Diego via Coursera
Have you wondered how information from physical devices in the real world gets communicated to Smartphone processors? Do you want to make informed design decisions about sampling frequencies and bit-width requirements for various kinds of sensors?  In this course, you will learn to interface common sensors and actuators to the DragonBoard™ 410c hardware.
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6.832x: Underactuated Robotics
Massachusetts Institute of Technology via edX
Robots today move far too conservatively, using control systems that attempt to maintain full control authority at all times. Humans and animals move much more aggressively by routinely executing motions which involve a loss of instantaneous control authority. Controlling nonlinear systems without complete control authority requires methods that can reason about and exploit the natural dynamics of our machines. This course introduces nonlinear dynamics and control of underactuated mechanical systems, with an emphasis on computational methods.
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Robotic Vision
Queensland University of Technology via EdCast
Robotic Vision introduces you to the field of computer vision and the mathematics and algorithms that underpin it.You’ll learn how to interpret images to determine the color, size, shape and position of objects in the scene.We’ll work with you to build an intelligent vision system that can recognise objects of different colours and shapes.
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SNU446.345.1x: Robot Mechanics and Control, Part I
Seoul National University via edX
This course provides a mathematical introduction to the mechanics and control of robots that can be modeled as kinematic chains. Topics covered include the concept of a robot’s configuration space and degrees of freedom, static grasp analysis, the description of rigid body motions, kinematics of open and closed chains, and the basics of robot control.
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Binaural Hearing for Robots
Inria (French Institute for Research in Computer Science and Automation) via France Université Numerique
This course will address fundamental issues in robot hearing; it will describe methodologies requiring two or more microphones embedded into a robot head, thus enabling sound-source localization, sound-source separation, and fusion of auditory and visual information.
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Learning and Cognition

SCIFI futuristic robots

Robotics: Perception
University of Pennsylvania via Coursera
How can robots perceive the world and their own movements so that they accomplish navigation and manipulation tasks? In this module, we will study how images and videos acquired by cameras mounted on robots are transformed into representations like features and optical flow. Such 2D representations allow us then to extract 3D information about where the camera is and in which direction the robot moves. You will come to understand how grasping objects is facilitated by the computation of 3D posing of objects and navigation can be accomplished by visual odometry and landmark-based localization.
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CNR101x: Cognitive Neuroscience Robotics – Part A
Osaka University via edX
Cognitive Neuroscience Robotics is an interdisciplinary area for development of new information and robot technology systems based on understanding higher functions of the human brain, with the integration of cognitive science, neuroscience, and robotics. This course introduces Cognitive Neuroscience Robotics with two approaches: the synthetic and the analytic approach.
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Robotics: Estimation and Learning
University of Pennsylvania via Coursera
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, and machine learning for planning and decision making.
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Mobile Robots and Autonomous Vehicles
Inria (French Institute for Research in Computer Science and Automation) via France Université Numerique
Mobile Robots are increasingly working in close interaction with human beings in environments as diverse as homes, hospitals, public spaces, public transportation systems and disaster areas. The situation is similar when it comes to Autonomous Vehicles, which are equipped with robot-like capabilities (sensing, decision and control).
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Applications & Programming

CS 8802, Artificial Intelligence for Robotics: Programming a Robotic Car
Stanford University via Udacity
Learn how to program all the major systems of a robotic car from the leader of Google and Stanford’s autonomous driving teams. This class will teach you basic methods in Artificial Intelligence, including: probabilistic inference, planning and search, localization, tracking and control, all with a focus on robotics.
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Artificial Intelligence Planning
University of Edinburgh via Coursera
The course aims to provide a foundation in artificial intelligence techniques for planning, with an overview of the wide spectrum of different problems and approaches, including their underlying theory and their applications.
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Bobby Brady

Bobby has successfully utilized MOOCs in his professional career twice, transitioning from the service industry to IT support and then again to Development. He now works full time as an edtech consultant and contractor helping students from all over the world learn React and JavaScript.

Comments 1

  1. Nguyen Trong Tung

    Nothing new, still


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