Underactuated Robotics Course – MIT

Robots today move far too conservatively, and accomplish only a fraction of the tasks and achieve a fraction of the performance that they are mechanically capable of.

In many cases, we are still fundamentally limited by control technology which matured on rigid robotic arms in structured factory environments. The study of underactuated robotics focuses on building control systems which use the natural dynamics of the machines in an attempt to achieve extraordinary performance in terms of speed, efficiency, or robustness.

 MIT 6.832 Underactuated Robotics, Spring 2009

Instructor: Russell Tedrake

Lecture 1 |Introduction

Lecture 2 | The Simple Pendulum

Lecture 3 | Optimal control of the double integrator

Lecture 4 | Optimal control of the double integrator (continued)

Lecture 5 | Numerical optimal control (dynamic programming)

Lecture 6 | Acrobot and cart-pole

Lecture 7 | Swing-up control of acrobot and cart-pole systems

Lecture 8 | Dynamic programming (DP) and policy search

Lecture 9 | Trajectory optimization

Lecture 10 | Trajectory stabilization and iterative linear quadratic regulator (iLQR)

Lecture 11 | Walking

Lecture 12 | Walking (continued)

Lecture 13 | Running

Lecture 14 | Feasible motion planning

Lecture 15 | Global policies from local policies

Lecture 16 | Introducing stochastic optimal control

Lecture 17 | Stochastic Gradient Descent
Instructor: John W. Roberts

Lecture 18 | Stochastic Gradient Descent 2
Instructor: John W. Roberts

Lecture 19 | Temporal difference learning

Lecture 20 | Temporal difference learning with function approximation

Lecture 21: Policy improvement

Lecture 22 | Actor-Critic methods

Lecture 23 | Case studies in computational underactuated control

Source: MIT OpenCourseWare

Russ Tedrake is a Full Professor in the Department of Electrical Engineering and Computer Science at MIT, and a member of the Computer Science and Artificial Intelligence Lab. He received his B.S.E. in Computer Engineering from the University of Michigan, Ann Arbor, in 1999, and his Ph.D. in Electrical Engineering and Computer Science from MIT in 2004, working with Sebastian Seung. After graduation, he spent a year with the MIT Brain and Cognitive Sciences Department as a Postdoctoral Associate. During his education, he has spent time at Microsoft, Microsoft Research, and the Santa Fe Institute.

Professor Tedrake’s research group is interested in underactuated motor control systems in animals and machines that are capable of executing dynamically dexterous tasks and interacting with uncertain environments. They believe that the design of these control systems is intimately related to the mechanical designs of their machines, and that tools from machine learning and optimal control can be used to exploit this coupling when classical control techniques fail. Current projects include robust and efficient bipedal locomotion on flat terrain, multi-legged locomotion over extreme terrain, flapping-winged flight, and feedback control for fluid dynamics.

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