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Affiliation

Istituto Dalle Molle di Studi Sull’Intelligenza Artificiale (IDSIA USI-SUPSI), Scuola Universitaria Professionale della Svizzera Italiana, DTI , Italy

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1 Papers

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

Topics: Machine Learning in Control Applications; System Identification; Modeling, Simulation and Architecture; System Modeling

Authors: Manuel Bazzi ¹ , Asad Shahid ² , Christopher Agia ³ , John Alora ³ , Marco Forgione ² , Dario Piga ² , Francesco Braghin ¹ , Marco Pavone ³ and Loris Roveda ^{2

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3}

Affiliations: ¹ Department of Mechanical Engineering, Politecnico di Milano, Italy , ² Istituto Dalle Molle di Studi Sull’Intelligenza Artificiale (IDSIA USI-SUPSI), Scuola Universitaria Professionale della Svizzera Italiana, DTI , Italy , ³ Stanford University, U.S.A.

Digital Object Identifier: 10.5220/0012945500003822

Proceedings of ICINCO 2024, ISBN: 978-989-758-717-7

Quick 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.