Morphological symmetries in robotics

作     者：Apraez, Daniel Ordoñez Turrisi, Giulio Kostic, Vladimir Martin, Mario Agudo, Antonio Moreno-Noguer, Francesc Pontil, Massimiliano Semini, Claudio Mastalli, Carlos 

作者机构： Italy Computational Statistics and Machine Learning IIT Italy Institut de Robòtica i Informàtica Industrial CSIC-UPC Spain Robot Motor Intelligence – Heriot-Watt University United Kingdom IHMC Robotics Florida Institute for Human & Machine Cognition United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Robots 

摘      要：We present a comprehensive framework for studying and leveraging morphological symmetries in robotic systems. These are intrinsic properties of the robot’s morphology, frequently observed in animal biology and robotics, which stem from the replication of kinematic structures and the symmetrical distribution of mass. We illustrate how these symmetries extend to the robot’s state space and both proprioceptive and exteroceptive sensor measurements, resulting in the equivariance of the robot’s equations of motion and optimal control policies. Thus, we recognize morphological symmetries as a relevant and previously unexplored physics-informed geometric prior, with significant implications for both data-driven and analytical methods used in modeling, control, estimation and design in robotics. For data-driven methods, we demonstrate that morphological symmetries can enhance the sample efficiency and generalization of machine learning models through data augmentation, or by applying equivariant/invariant constraints on the model’s architecture. In the context of analytical methods, we employ abstract harmonic analysis to decompose the robot’s dynamics into a superposition of lower-dimensional, independent dynamics. We substantiate our claims with both synthetic and real-world experiments conducted on bipedal and quadrupedal robots. Lastly, we introduce the repository MORPHOSYMM to facilitate the practical use of the theory and applications outlined in this *** Codes 68T40 © 2024, CC BY.