A Data-Driven Approach to Geometric Modeling of Systems with Low-Bandwidth Actuator Dynamics

TL;DR

This paper introduces a data-driven geometric mechanics framework for modeling systems with low-bandwidth actuators, enabling accurate system identification and control optimization for soft robots and biomedically relevant micromachines.

Contribution

It presents a novel geometric mechanics-based modeling method for low-bandwidth actuated systems, applicable to soft robots and chemo-mechanical micromachines, with an iterative control optimization approach.

Findings

Models accurately capture system shape and movement dynamics.

Effective in simulating hydrogel crawler behavior.

Optimizes control signals for improved locomotion.

Abstract

It is challenging to perform system identification on soft robots due to their underactuated, high-dimensional dynamics. In this work, we present a data-driven modeling framework, based on geometric mechanics (also known as gauge theory) that can be applied to systems with low-bandwidth control of the system's internal configuration. This method constructs a series of connected models comprising actuator and locomotor dynamics based on data points from stochastically perturbed, repeated behaviors. By deriving these connected models from general formulations of dissipative Lagrangian systems with symmetry, we offer a method that can be applied broadly to robots with first-order, low-pass actuator dynamics, including swelling-driven actuators used in hydrogel crawlers. These models accurately capture the dynamics of the system shape and body movements of a simplified swimming robot model. We further apply our approach to a stimulus-responsive hydrogel simulator that captures the complexity of chemo-mechanical interactions that drive shape changes in biomedically relevant micromachines. Finally, we propose an approach of numerically optimizing control signals by iteratively refining models, which is applied to optimize the input waveform for the hydrogel crawler. This transfer to realistic environments provides promise for applications in locomotor design and biomedical engineering.