Bayesian Optimization Meets Riemannian Manifolds in Robot Learning

11 Oct 2019Noémie JaquierLeonel RozoSylvain CalinonMathias Bürger

Bayesian optimization (BO) recently became popular in robotics to optimize control parameters and parametric policies in direct reinforcement learning due to its data efficiency and gradient-free approach. However, its performance may be seriously compromised when the parameter space is high-dimensional... (read more)

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