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

TL;DR

This paper introduces a learning-based compact FEM model for soft robots that enables efficient direct and inverse kinematics, facilitating real-time control and design improvements.

Contribution

It presents a novel method to learn a condensed FEM model from data, enabling real-time control and inverse kinematics for soft robots.

Findings

The compact model accurately predicts robot behavior.

It enables efficient computation of direct and inverse kinematics.

The approach is demonstrated on a soft gripper with promising results.

Abstract

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 learning-based approach to obtain a compact but sufficiently rich mechanical representation. Our choice is based on nonlinear compliance data in the actuator/effector space provided by a condensation of the FEM model. We demonstrate that this compact model can be learned with a reasonable amount of data and, at the same time, be very efficient in terms of modeling, since we can deduce the direct and inverse kinematics of the robot. We also show how to couple some models learned individually in particular on an example of a gripper composed of two soft fingers. Other results are shown by comparing the inverse model derived from the full FEM model and the one from the compact learned version. This work opens new perspectives, namely for the embedded control of soft robots, but also for their design. These perspectives are also discussed in the paper.