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We present an method for generating high-quality plans for a robot arm with many degrees of freedom based on Learning Dimensional Descent (LDD), a recently-developed algorithm for planning in high-dimensional spaces based on machine learning and optimization techniques. Unlike other approaches used to solve this problem, our method optimizes a well-defined objective and can be shown to generate optimal...
We demonstrate how to find high-quality motion plans for high-dimensional holonomic systems efficiently using dynamic programming in a learned subspace of vastly reduced dimension. Our approach (SLASHDP) learns the low-dimensional cost structure of an optimal control problem via an efficient spectral method. This structure results in a symmetric value function that serves as a an efficiently-computable...
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...
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