Etienne Ménager — accepted AI-conference papers

2023

Direct and inverse modeling of soft robots by learning a condensed FEM model

Etienne Ménager, Tanguy Navez, Olivier Goury, Christian Duriez

ICRA 2023poster

The Finite Element Method (FEM) is a powerful modeling tool for predicting the behavior of soft robots. However, its use for control can be difficult for non-specialists of numerical computation: it requires an optimization of the computation to make it real-time. In this paper, we propose a learnin…

Cited by 7 Source ↗Scholar ↗

2023

Toward the Use of Proxies for Efficient Learning Manipulation and Locomotion Strategies on Soft Robots

Etienne Ménager, Quentin Peyron, Christian Duriez

RA-L 2023

Soft robots are naturally designed to perform safe interactions with their environment, like locomotion and manipulation. In the literature, there are now many concepts, often bio-inspired, to propose new modes of locomotion or grasping. However, a methodology for implementing motion planning of the

Cited by 4 Source ↗Scholar ↗