Summary
This paper proposes a method for geometrically regularizing autoencoders to more accurately model stochastic dynamical systems. It addresses limitations of current techniques, such as the scaling issues of local-chart methods like ATLAS and the poor geometric constraint of standard autoencoders, which lead to errors in learning system dynamics on low-dimensional manifolds. The goal is to improve the fidelity of reduced simulators by better preserving tangent-bundle geometry and accurately capturing drift and diffusion.
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[Paper] Ruka-v2: Tendon Driven Open-Source Dexterous Hand with Wrist and Abduction for Robot Learning
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