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Principles of Humanoid Locomotion Control

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Understanding the control forces that drive humans and animals is fundamental to describing their movement. Good models of control would be informative for many fields. Although physics-based methods hold promise for creating animation, they have long been considered too difficult to design and control. Likewise, physical motion models, if developed, could be very valuable to human pose tracking and recognition in computer vision.

I will outline the main problems of human motion modeling, and describe some principles of humanoid motion from the biomechanics literature. Based on these principles, I will then present a new approach to control of physics-based characters based on high-level features of human movement.

These controllers provide unprecedented flexibility and generality in real-time character control: they capture many natural properties of human movement, they can be easily modified and applied to new characters, and they can handle a variety of different terrains and tasks, all within a single control strategy.

Until very recently, even making a controller walk without falling down was extraordinarily difficult. This is no longer the case. Our work, together with other recent results in this area, suggests that we are now ready to make great strides in locomotion.

This talk is part of the Microsoft Research Machine Learning and Perception Seminars series.

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