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ICRA
2010
IEEE

Learning and planning high-dimensional physical trajectories via structured Lagrangians

9 years 9 months ago
Learning and planning high-dimensional physical trajectories via structured Lagrangians
— We consider the problem of finding sufficiently simple models of high-dimensional physical systems that are consistent with observed trajectories, and using these models to synthesize new trajectories. Our approach models physical trajectories as least-time trajectories realized by free particles moving along the geodesics of a curved manifold, reminiscent of the way light rays obey Fermat’s principle of least time. Finding these trajectories, unfortunately, requires finding a minimumcost path in a high-dimensional space, which is generally a computationally intractable problem. In this work we show that this high-dimensional planning problem can often be solved nearly optimally in practice via deterministic search, as long as we can find a certain low-dimensional structure in the Lagrangian that describes our observed trajectories. This lowdimensional structure additionally makes it feasible to learn an estimate of a Lagrangian that is consistent with the observed trajectori...
Paul Vernaza, Daniel D. Lee, Seung-Joon Yi
Added 26 Jan 2011
Updated 26 Jan 2011
Type Journal
Year 2010
Where ICRA
Authors Paul Vernaza, Daniel D. Lee, Seung-Joon Yi
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