title: Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control.
abstract: Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accurate knowledge of manipulator kinematics or dynamics. However, mechanical and analytical models do not capture all aspects of a plant's intrinsic properties and there remain unmodeled dynamics due to varying parameters, unmodeled friction or soft materials. In this context, machine learning is an alternative suitable technique to extract non-linear plant models from data. However, fully data-based models suffer from inaccuracies as well and are inefficient if they include learning of well known analytical models. This paper thus argues that feed-forward control based on hybrid models comprising an analytical model and a learned error model can significantly improve modeling accuracy. Hybrid modeling here serves the purpose to combine the best of the two modeling worlds. The hybrid modeling methodology is described and the approach is demonstrated for two typical problems in robotics, i.e., inverse kinematics control and computed torque control. The former is performed for a redundant soft robot and the latter for a rigid industrial robot with redundant degrees of freedom, where a complete analytical model is not available for any of the platforms.
authors.affiliation: Fraunhofer Research Institute for Mechatronic Systems Design (IEM), Zukunftsmeile 1, 33102 Paderborn, Germany. felix.reinhart@iem.fraunhofer.de.
authors.forename: René Felix
authors.initials: RF
authors.lastname: Reinhart
authors.affiliation: Institute for Robotics and Process Control (IRP), Technische Universität Braunschweig, Mühlenpfordstraße 23, 38106 Braunschweig, Germany. zshareef@rob.cs.tu-bs.de.
authors.forename: Zeeshan
authors.initials: Z
authors.lastname: Shareef
authors.affiliation: Institute for Robotics and Process Control (IRP), Technische Universität Braunschweig, Mühlenpfordstraße 23, 38106 Braunschweig, Germany. jsteil@rob.cs.tu-bs.de.
authors.forename: Jochen Jakob
authors.initials: JJ
authors.lastname: Steil
keywords: data-driven modeling; error models; inverse dynamics; inverse kinematics; learning feed-forward control; machine learning; mechanical modeling; soft robot