Condensed FEM Model Learning

This plugin for the open-source simulation framework SOFA contains components for learning a condensed FEM model from a soft robot SOFA scene. Authors also provide an implementation for leveraging the learned model for control, embedded control, calibration and design optimization applications.

The Finite Element Method (FEM) is a powerful modeling tool for predicting the behavior of soft robots. However, its computation time is a limitation when considering robotics applications. T. Navez, E. Ménager et al. propose a learning-based approach based on condensation of the FEM model for quickly handling all kind of constraints and in particular contacts. This plugin is envisioned as a general framework for modeling, control and design of soft robots based on a condensed FEM model. It provides a platform for generating simulation data using SOFA, training Neural Networks for predicting the condensed FEM Model, as well as for leveraging it in several applications: Inverse Control, Embedded Control and Design Optimization. Moreover, several examples are available for illustrating all aforementioned applications.

Related publications

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

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