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Learning Forward and Inverse Kinematics Maps Efficiently

When learning forward and inverse kinematics maps of manipulators, usually little attention is paid to data-efficiency, i.e., the accuracy gained per action-outcome sample. This paper examines properties of popular (online) learning techniques and demonstrates that - regardless of the employed exploration strategy - the structure of kinematics mappings does not allow for a practically viable trade-off between the number of samples and the resulting approximation error for manipulators with more than a few DoFs - unless tailored parametric models are employed. We discuss suitable choices for these parametric models for both rigid and elastic discretely-actuated robots and compare their data -efficiency to that of popular exploratory learning approaches relying on non-parametric models. Our theoretical considerations are confirmed by various experimental results for inverse kinematics mappings of rigid and omnielastic manipulators.