An Experimental Evaluation of Bayesian Optimization on Bipedal Locomotion

The design of gaits and corresponding control policies for bipedal walkers is a key challenge in robot locomotion. Even when a viable controller parametrization already exists, finding near-optimal parameters can be daunting. The use of automatic gait optimization methods greatly reduces the need for human expertise and time-consuming design processes. In this paper, we experimentally evaluate Bayesian optimization for gait optimization of a real bipedal walker. By performing more than 1800 experimental evaluations, we compare Bayesian optimization with various acquisition functions. Additionally, we study the effects of using fixed hyperparameters instead of automatically optimize them.

[1]  Harold J. Kushner,et al.  A New Method of Locating the Maximum Point of an Arbitrary Multipeak Curve in the Presence of Noise , 1964 .

[2]  Tao Wang,et al.  Automatic Gait Optimization with Gaussian Process Regression , 2007, IJCAI.

[3]  Howie Choset,et al.  Using response surfaces and expected improvement to optimize snake robot gait parameters , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[5]  Jorge Nocedal,et al.  A Limited Memory Algorithm for Bound Constrained Optimization , 1995, SIAM J. Sci. Comput..

[6]  C. D. Perttunen,et al.  Lipschitzian optimization without the Lipschitz constant , 1993 .

[7]  D. Dennis,et al.  A statistical method for global optimization , 1992, [Proceedings] 1992 IEEE International Conference on Systems, Man, and Cybernetics.

[8]  D. Lizotte,et al.  An experimental methodology for response surface optimization methods , 2012, J. Glob. Optim..

[9]  Nando de Freitas,et al.  A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning , 2010, ArXiv.

[10]  Philipp Hennig,et al.  Entropy Search for Information-Efficient Global Optimization , 2011, J. Mach. Learn. Res..

[11]  Donald R. Jones,et al.  A Taxonomy of Global Optimization Methods Based on Response Surfaces , 2001, J. Glob. Optim..