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Sample efficient optimization for learning controllers for bipedal locomotion
2016
2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids)
Learning policies for bipedal locomotion can be difficult, as experiments are expensive and simulation does not usually transfer well to hardware. To counter this, we need al- gorithms that are sample efficient and inherently safe. Bayesian Optimization is a powerful sample-efficient tool for optimizing non-convex black-box functions. However, its performance can degrade in higher dimensions. We develop a distance metric for bipedal locomotion that enhances the sample-efficiency of Bayesian
doi:10.1109/humanoids.2016.7803249
dblp:conf/humanoids/AntonovaRA16
fatcat:pypte6ju4rhpjp4z6uiw64ztv4