Dynamic Shape Control of Soft Robots Enabled by Data-Driven Model Reduction

TL;DR

This paper compares data-driven model reduction techniques for controlling the shape of soft robots, demonstrating that Lagrangian operator inference yields more accurate control in simulations and physical analogs.

Contribution

It introduces a comparative analysis of three model reduction methods for soft robot control, highlighting the effectiveness of LOpInf in dynamic shape control tasks.

Findings

LOpInf-based models outperform others in tracking accuracy

Data-driven models enable effective control of high-dimensional soft robot dynamics

The study validates models through simulations and physical experiments

Abstract

Soft robots have shown immense promise in settings where they can leverage dynamic control of their entire bodies. However, effective dynamic shape control requires a controller that accounts for the robot's high-dimensional dynamics--a challenge exacerbated by a lack of general-purpose tools for modeling soft robots amenably for control. In this work, we conduct a comparative study of data-driven model reduction techniques for generating linear models amendable to dynamic shape control. We focus on three methods--the eigensystem realization algorithm, dynamic mode decomposition with control, and the Lagrangian operator inference (LOpInf) method. Using each class of model, we explored their efficacy in model predictive control policies for the dynamic shape control of a simulated eel-inspired soft robot in three experiments: 1) tracking simulated reference trajectories guaranteed to be feasible, 2) tracking reference trajectories generated from a biological model of eel kinematics, and 3) tracking reference trajectories generated by a reduced-scale physical analog. In all experiments, the LOpInf-based policies generated lower tracking errors than policies based on other models.