Underactuated Robotics | MIT OCW Course

MIT

Explore the world of underactuated robotic systems, learn about nonlinear control, optimization-based control, and motion planning with this MIT OpenCourseWare course.

University CoursesControl SystemsRobotics

Introduction

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 au

screenshot

Highlights

  • Focuses on underactuated robotic systems, which have fewer control inputs than degrees of freedom
  • Covers topics such as nonlinear control, optimization-based control, and motion planning
  • Includes hands-on projects and video lectures by Professor Russell Tedrake

Recommendation

This course is recommended for graduate students and researchers interested in the field of robotics, particularly those looking to explore more aggressive and efficient robotic motion. The course's emphasis on underactuated systems and optimization-based control techniques provides valuable insights for developing advanced robotic systems.

How GetVM Works

Learn by Doing from Your Browser Sidebar

Access from Browser Sidebar

Access from Browser Sidebar

Simply install the browser extension and click to launch GetVM directly from your sidebar.

Select Your Playground

Select Your Playground

Choose your OS, IDE, or app from our playground library and launch it instantly.

Learn and Practice Side-by-Side

Learn and Practice Side-by-Side

Practice within the VM while following tutorials or videos side-by-side. Save your work with Pro for easy continuity.

Explore Similar Hands-on Tutorials

Artificial Intelligence for Robotics | MOOC - Udacity

0
University CoursesArtificial IntelligenceRobotics
Learn the fundamentals of AI and its applications in robotics through hands-on projects and expert-led instruction from Udacity.

Data Science for Dynamical Systems | Practical Applications, Reinforcement Learning

0
University CoursesControl SystemsData ScienceMachine Learning
Explore data science techniques for analyzing and controlling dynamic systems with this comprehensive course. Covers system identification, model predictive control, and more.

Mobile Sensing and Robotics | Bonn University Course

0
University CoursesRobotics
Comprehensive course on mobile sensing and robotics, covering sensor technologies, localization, mapping, and navigation. Hands-on demonstrations and practical exercises.

Introduction to Feedback Control Systems | CMU 16-299 | Spring 2022

0
University CoursesControl TheoryRobotics
Explore the fundamentals of feedback control systems with intuitive concepts and hands-on lab components in this course by Chris Atkeson at Carnegie Mellon University.

Robot Dynamics | Advanced Robotics Course - CMU

0
University CoursesControl SystemsRobotics
Explore the fundamental principles and techniques of robot dynamics, including kinematics, kinetics, and control. Gain hands-on experience with practical examples and case studies.

Optimal Control Theory | Carnegie Mellon University

0
University CoursesControl SystemsRobotics
Comprehensive overview of optimal control theory, covering continuous-time and discrete-time systems. Hands-on exercises and insights from experienced CMU instructors.

Model Predictive Control Course | EPFL Engineering

0
University CoursesControl SystemsRobotics
Comprehensive course on the fundamentals of model predictive control (MPC), taught by a leading expert at EPFL. Hands-on exercises and industry-standard software.

Autonomous Vehicle System Engineering | UIUC CS 588 by David Forsyth

0
University CoursesComputer VisionDeep LearningRobotics
Comprehensive course on autonomous vehicle design, covering deep learning, computer vision, and control theory. Hands-on projects and simulations for practical experience.

Network Systems, Dynamics & Control | UC Santa Barbara ME 269

0
University CoursesControl TheoryRobotics
Comprehensive lectures on network systems, dynamics, and control by Professor Francesco Bullo at UC Santa Barbara. Suitable for students and professionals in engineering, math, and computer science.