Lecture 1 | MIT 6.832 Underactuated Robotics, Spring 2009
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Lecture 2 | MIT 6.832 Underactuated Robotics, Spring 2009
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Lecture 3 | MIT 6.832 Underactuated Robotics, Spring 2009
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Lecture 4 | MIT 6.832 Underactuated Robotics, Spring 2009
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Lecture 5 | MIT 6.832 Underactuated Robotics, Spring 2009
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Lecture 6 | MIT 6.832 Underactuated Robotics, Spring 2009
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Lecture 7 | MIT 6.832 Underactuated Robotics, Spring 2009
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Lecture 8 | MIT 6.832 Underactuated Robotics, Spring 2009
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Lecture 9 | MIT 6.832 Underactuated Robotics, Spring 2009
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Lecture 10 | MIT 6.832 Underactuated Robotics, Spring 2009
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Lecture 11 | MIT 6.832 Underactuated Robotics, Spring 2009
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Lecture 12 | MIT 6.832 Underactuated Robotics, Spring 2009
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Lecture 13 | MIT 6.832 Underactuated Robotics, Spring 2009
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Lecture 14 | MIT 6.832 Underactuated Robotics, Spring 2009
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Lecture 15 | MIT 6.832 Underactuated Robotics, Spring 2009
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Lecture 16 | MIT 6.832 Underactuated Robotics, Spring 2009
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Lecture 17 | MIT 6.832 Underactuated Robotics, Spring 2009
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Lecture 18 | MIT 6.832 Underactuated Robotics, Spring 2009
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Lecture 19 | MIT 6.832 Underactuated Robotics, Spring 2009
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Lecture 20 | MIT 6.832 Underactuated Robotics, Spring 2009
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Lecture 21 | MIT 6.832 Underactuated Robotics, Spring 2009
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Lecture 22 | MIT 6.832 Underactuated Robotics, Spring 2009
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Lecture 23 | MIT 6.832 Underactuated Robotics, Spring 2009
Description:
Instructor: Russell Tedrake. 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 discusses nonlinear dynamics and control of underactuated mechanical systems, with an emphasis on machine learning methods. Topics include nonlinear dynamics of passive robots (walkers, swimmers, flyers), motion planning, partial feedback linearization, energy-shaping control, analytical optimal control, reinforcement learning/approximate optimal control, and the influence of mechanical design on control. Discussions include examples from biology and applications to legged locomotion, compliant manipulation, underwater robots, and flying machines.
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