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Authors: Arne Hasselbring 1 ; Udo Frese 1 ; 2 and Thomas Röfer 1 ; 2

Affiliations: 1 Deutsches Forschungszentrum für Künstliche Intelligenz, Cyber-Physical Systems, Bremen, Germany ; 2 Universität Bremen, Fachbereich 3 – Mathematik und Informatik, Bremen, Germany

Keyword(s): Trajectory Optimization, Machine Learning, Robot Dynamics.

Abstract: This paper is concerned with learning to compute optimal robot trajectories for a given parametrized task. We propose to train a neural network directly with the model-based loss function that defines the optimization goal for the trajectories. This is opposed to computing optimal trajectories and learning from that data and opposed to using reinforcement learning. As the resulting optimization problem is very ill-conditioned, we propose a preconditioner based on the inverse Hessian of the part of the loss related to the robot dynamics. We also propose how to integrate this into a commonly used dataflow-based auto-differentiation framework (TensorFlow). Thus it keeps the framework’s generality regarding the definition of losses, layers, and dataflow. We show a simulation case study of a robot arm catching a flying ball and keeping it in the torus shaped bat. The method can also optimize “voluntary task parameters”, here the starting configuration of the robot.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Hasselbring, A.; Frese, U. and Röfer, T. (2022). Learning Optimal Robot Ball Catching Trajectories Directly from the Model-based Trajectory Loss. In Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - ICINCO; ISBN 978-989-758-585-2; ISSN 2184-2809, SciTePress, pages 201-208. DOI: 10.5220/0011279000003271

@conference{icinco22,
author={Arne Hasselbring. and Udo Frese. and Thomas Röfer.},
title={Learning Optimal Robot Ball Catching Trajectories Directly from the Model-based Trajectory Loss},
booktitle={Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - ICINCO},
year={2022},
pages={201-208},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011279000003271},
isbn={978-989-758-585-2},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - ICINCO
TI - Learning Optimal Robot Ball Catching Trajectories Directly from the Model-based Trajectory Loss
SN - 978-989-758-585-2
IS - 2184-2809
AU - Hasselbring, A.
AU - Frese, U.
AU - Röfer, T.
PY - 2022
SP - 201
EP - 208
DO - 10.5220/0011279000003271
PB - SciTePress

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