Fig. 5
From: A development cycle for automated self-exploration of robot behaviors
Clustering overview. A representative capability set, along with the corresponding parameters θ and capability function model is provided by the exploration. Transformation functions ffk are applied to map to feature spaces Fk. In these feature spaces, clustering is performed. The labelled clusters are used to train probabilistic generative models on the parameter space Θ, s.t. clusters can be stored in an efficient and expressive way. When sampling from the generative cluster models, parameters θ are generated that lead to capabilities in the intended cluster. The mapping from parameters to a capability is mediated by the capability function model and the execution loop (see Fig. 4). During training, sampling of parameters and generation of new capabilities is used to verify model performance