RoboMorph: In-Context Meta-Learning for Robot Dynamics Modeling

Manuel Bazzi, Asad Shahid, Christopher Agia, John Alora, Marco Forgione, Dario Piga, Francesco Braghin, Marco Pavone, Loris Roveda, Loris Roveda

2024

Abstract

The landscape of Deep Learning has experienced a major shift with the pervasive adoption of Transformer-based architectures, particularly in Natural Language Processing (NLP). Novel avenues for physical applications, such as solving Partial Differential Equations and Image Vision, have been explored. However, in challenging domains like robotics, where high non-linearity poses significant challenges, Transformer-based applications are scarce. While Transformers have been used to provide robots with knowledge about high-level tasks, few efforts have been made to perform system identification. This paper proposes a novel methodology to learn a meta-dynamical model of a high-dimensional physical system, such as the Franka robotic arm, using a Transformer-based architecture without prior knowledge of the system’s physical parameters. The objective is to predict quantities of interest (end-effector pose and joint positions) given the torque signals for each joint. This prediction can be useful as a component for Deep Model Predictive Control frameworks in robotics. The meta-model establishes the correlation between torques and positions and predicts the output for the complete trajectory. This work provides empirical evidence of the efficacy of the in-context learning paradigm, suggesting future improvements in learning the dynamics of robotic systems without explicit knowledge of physical parameters. Code, videos, and supplementary materials can be found at project website.

Paper Citation

in Harvard Style

Bazzi M., Shahid A., Agia C., Alora J., Forgione M., Piga D., Braghin F., Pavone M. and Roveda L. (2024). RoboMorph: In-Context Meta-Learning for Robot Dynamics Modeling. In Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO; ISBN 978-989-758-717-7, SciTePress, pages 149-156. DOI: 10.5220/0012945500003822

in Bibtex Style

@conference{icinco24,
author={Manuel Bazzi and Asad Shahid and Christopher Agia and John Alora and Marco Forgione and Dario Piga and Francesco Braghin and Marco Pavone and Loris Roveda},
title={RoboMorph: In-Context Meta-Learning for Robot Dynamics Modeling},
booktitle={Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO},
year={2024},
pages={149-156},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012945500003822},
isbn={978-989-758-717-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO
TI - RoboMorph: In-Context Meta-Learning for Robot Dynamics Modeling
SN - 978-989-758-717-7
AU - Bazzi M.
AU - Shahid A.
AU - Agia C.
AU - Alora J.
AU - Forgione M.
AU - Piga D.
AU - Braghin F.
AU - Pavone M.
AU - Roveda L.
PY - 2024
SP - 149
EP - 156
DO - 10.5220/0012945500003822
PB - SciTePress