A concise guide to modelling the physics of embodied intelligence in soft robotics - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Nature Reviews Physics Year : 2022

A concise guide to modelling the physics of embodied intelligence in soft robotics

Abstract

Embodied intelligence, or intelligence that requires and leverages a physical body, is ubiquitous in biological systems, both in animals and plants. Through embodied intelligence, biological systems efficiently interact with and use their surrounding environment to let adaptive behaviour emerge. In soft robotics, this is a well-known paradigm, whose mathematical description and consequent computational modelling remain elusive. We argue that filling this gap will enable full uptake of embodied intelligence in soft robots. The resulting models can be used for design and control purposes. In this paper, we provide a concise guide to the main mathematical modelling approaches, and consequent computational modeling strategies, that can be used to describe soft robots and their physical interactions with the surrounding environment, including fluid and solid media. The goal of this perspective is to convey the challenges and opportunities within the context of modeling the physical interactions underpinning embodied intelligence. We emphasize that interdisciplinary work is required, especially in the context of fully coupled robot-environment interaction modeling. Promoting this dialogue across disciplines is a necessary step to further advance the field of soft robotics.
Fichier principal
Vignette du fichier
NRP_soft_modeling.pdf (7.69 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03921606 , version 1 (03-01-2023)

Identifiers

Cite

Gianmarco Mengaldo, Federico Renda, Steven L Brunton, Moritz Bächer, Marcello Calisti, et al.. A concise guide to modelling the physics of embodied intelligence in soft robotics. Nature Reviews Physics, 2022, 4 (9), pp.595-610. ⟨10.1038/s42254-022-00481-z⟩. ⟨hal-03921606⟩
174 View
485 Download

Altmetric

Share

Gmail Facebook X LinkedIn More