机器人变形:机器人动力学建模的上下文元学习

Manuel Bianchi Bazzi, Asad Ali Shahid, Christopher Agia, John Alora, Marco Forgione, Dario Piga, Francesco Braghin, Marco Pavone, Loris Roveda
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引用次数: 0

摘要

随着基于变压器的架构被广泛采用,深度学习的格局发生了重大变化,尤其是在自然语言处理(NLP)领域。然而,在机器人等具有挑战性的领域,高非线性带来了重大挑战,基于变换器的应用却很少。虽然变换器已被用于为机器人提供有关高级任务的知识,但很少有人致力于进行系统识别。本文提出了一种新颖的方法,利用基于变压器的架构学习高维物理系统(如弗兰卡机械臂)的元动力学模型,而无需事先了解系统的物理参数。目标是根据每个关节的扭矩信号预测相关量(末端执行器姿势和关节位置)。这种预测可以作为机器人深度模型预测控制框架的一个组成部分。元模型建立了扭矩和位置之间的相关性,并预测了完整轨迹的输出。这项工作为情境学习范式的有效性提供了实证证据,为未来在没有明确物理参数知识的情况下学习机器人系统的动力学提出了改进建议。代码、视频和补充材料请访问项目网站。见https://sites.google.com/view/robomorph/
本文章由计算机程序翻译,如有差异,请以英文原文为准。
RoboMorph: In-Context Meta-Learning for Robot Dynamics Modeling
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. See https://sites.google.com/view/robomorph/
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