Simple Models, Real Swimming: Digital Twins for Tendon-Driven Underwater Robots

TL;DR

This paper introduces a simplified, efficient digital twin model for tendon-driven underwater robots that accurately replicates real swimming behavior and enables scalable learning and control.

Contribution

It presents a novel, stateless hydrodynamics model in MuJoCo that matches experimental data and outperforms classical models, facilitating reinforcement learning for underwater robots.

Findings

Model matches five fluid parameters to real swimming trajectories

Generalizes well to unseen actuation frequencies

Achieves 93% success rate in target tracking tasks

Abstract

Mimicking the graceful motion of swimming animals remains a core challenge in soft robotics due to the complexity of fluid-structure interaction and the difficulty of controlling soft, biomimetic bodies. Existing modeling approaches are often computationally expensive and impractical for complex control or reinforcement learning needed for realistic motions to emerge in robotic systems. In this work, we present a tendon-driven fish robot modeled in an efficient underwater swimmer environment using a simplified, stateless hydrodynamics formulation implemented in the widespread robotics framework MuJoCo. With just two real-world swimming trajectories, we identify five fluid parameters that allow a matching to experimental behavior and generalize across a range of actuation frequencies. We show that this stateless fluid model can generalize to unseen actuation and outperform classical analytical models such as the elongated body theory. This simulation environment runs faster than real-time and can easily enable downstream learning algorithms such as reinforcement learning for target tracking, reaching a 93% success rate. Due to the simplicity and ease of use of the model and our open-source simulation environment, our results show that even simple, stateless models -- when carefully matched to physical data -- can serve as effective digital twins for soft underwater robots, opening up new directions for scalable learning and control in aquatic environments.