Learning low-dimensional structure in Lagrangian systems
Prof. Daniel D. Lee (The University of Pennsylvania, Philadelphia, PA. , USA)
May 17, 2011 2:00 PM
Learning the nature of a physical system is a problem that presents many challenges and opportunities due to the unique structure associated with such systems. Many physical systems in engineering are high-dimensional, which prohibits the application of standard learning methods to such problems. We have recently worked on solving such learning problems by identifying their low-dimensional Lagrangian structure, and the associated inference problem of solving a high-dimensional minimum-cost path problem by exploiting the symmetry of the problem. These methods are demonstrated via application to learning structure in high-dimensional human motion and optimal motion planning for highly articulated robot mechanisms. ’s brain cell using the pattern matching techniques and also to develop an automation program which can detect contact points of two materials precisely in nano-scale.
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