Multi-segment Soft Robot Control via Deep Koopman-based Model Predictive Control

TL;DR

This paper introduces a Deep Koopman-based Model Predictive Control framework that linearizes complex soft robot dynamics for precise trajectory tracking, demonstrated through real-world experiments on a multi-segment soft robot.

Contribution

It presents a novel DK-MPC approach that combines deep learning and Koopman operator theory to control high-dimensional soft robot dynamics effectively.

Findings

Achieves high-precision control in real-world experiments

Successfully linearizes nonlinear soft robot dynamics

Demonstrates potential for future soft robot applications

Abstract

Soft robots, compared to regular rigid robots, as their multiple segments with soft materials bring flexibility and compliance, have the advantages of safe interaction and dexterous operation in the environment. However, due to its characteristics of high dimensional, nonlinearity, time-varying nature, and infinite degree of freedom, it has been challenges in achieving precise and dynamic control such as trajectory tracking and position reaching. To address these challenges, we propose a framework of Deep Koopman-based Model Predictive Control (DK-MPC) for handling multi-segment soft robots. We first employ a deep learning approach with sampling data to approximate the Koopman operator, which therefore linearizes the high-dimensional nonlinear dynamics of the soft robots into a finite-dimensional linear representation. Secondly, this linearized model is utilized within a model predictive control framework to compute optimal control inputs that minimize the tracking error between the desired and actual state trajectories. The real-world experiments on the soft robot "Chordata" demonstrate that DK-MPC could achieve high-precision control, showing the potential of DK-MPC for future applications to soft robots.