Feb 16, 2016 @ 19:19
Coursera, an online learning platform, started Robotics Specialization Courses. Right now this consists of 5 paid courses ($29 per course) in robotics field. The Robotics Specialization is created by Penn University.
About Robotics Specialization
The Robotics Specialization introduces you to how robots sense and reason about the world they live, how they plan three dimensional movements in a dynamic environment and how they fly or run while adapting to uncertainties in the environment. You will be exposed to real world examples with drones, legged robots and driverless cars. The courses build towards a capstone in which you will learn how to program robots to perform a variety of tasks in unstructured, dynamic environments. Watch Intro of course.
Course -1
Robotics: Aerial Robotics
Current session: Feb 15 — Mar 21
About the Course
How can we create agile micro aerial vehicles that are able to operate autonomously in cluttered indoor and outdoor environments? You will gain an introduction to the mechanics of flight and the design of quadrotor flying robots and will be able to develop dynamic models, derive controllers, and synthesize planners for operating in three dimensional environments. You will be exposed to the challenges of using noisy sensors for localization and maneuvering in complex, three-dimensional environments. Finally, you will gain insights through seeing real world examples of the possible applications and challenges for the rapidly-growing drone industry.
Course -2
Robotics: Computational Motion Planning
Current session: Feb 15 — Mar 21
About the Course
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. In this course we will consider the problem of how a robot decides what to do to achieve its goals. This problem is often referred to as Motion Planning and it has been formulated in various ways to model different situations. You will learn some of the most common approaches to addressing this problem including graph-based methods, randomized planners and artificial potential fields. Throughout the course, we will discuss the aspects of the problem that make planning challenging.
Course -3
Robotics: Mobility
Starts March 2016
About the Course
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. We develop an approach to composing simple dynamical abstractions that partially automate the generation of complicated sensorimotor programs. Specific topics that will be covered include: mobility in animals and robots, kinematics and dynamics of legged machines, and design of dynamical behavior via energy landscapes.
Course -4
Robotics: Perception
Starts April 2016
About the Course
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.
Course -5
Robotics: Estimation and Learning
Starts May 2016
About the Course
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.
Go to the course