Bayesian Optimization for Contextual Policy Search

Jan Hendrik Metzen, Alexander Fabisch, Jonas Hansen

In: Proceedings of the Second Machine Learning in Planning and Control of Robot Motion Workshop. IROS Workshop: Machine Learning in Planning and Control of Robot Motion (IROS MLPC-2015) 2nd located at IROS 2015 October 2 Hamburg Germany IROS 2015.


Contextual policy search allows adapting robotic movement primitives to different situations. For instance, a locomotion primitive might be adapted to different terrain inclinations or desired walking speeds. Such an adaptation is often achievable by modifying a relatively small number of hyperparameters; however, learning when performed on an actual robotic system is typically restricted to a relatively small number of trials. In black-box optimization, Bayesian optimization is a popular global search approach for addressing such problems with low-dimensional search space but expensive cost function. We present an extension of Bayesian optimization to contextual policy search. Preliminary results suggest that Bayesian optimization outperforms local search approaches on low-dimensional contextual policy search problems.


mlpc.pdf (pdf, 194 KB )

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz