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

2026-02-26

AI Summary

This work addresses the challenge of achieving efficient and high-fidelity control of soft underwater robots, which is hindered by strong nonlinear fluid–structure interactions. To this end, the authors propose a lightweight, stateless hydrodynamic model that requires only two real-world trajectories to calibrate five key fluid parameters. Integrated into the MuJoCo simulation framework, this approach enables a computationally efficient digital twin system with strong generalization capabilities. The method significantly outperforms conventional analytical models—such as slender-body theory—in both accuracy and speed, achieving faster-than-real-time simulation suitable for reinforcement learning. In target-tracking tasks, the system attains a 93% success rate and demonstrates robust generalization to unseen actuation frequencies.

Technology Category

• Intelligent Robots: State Estimation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• soft robotics
• fluid-structure interaction
• underwater locomotion
• digital twins
• biomimetic swimming

Innovation

• digital twin
• tendon-driven robot
• stateless hydrodynamics
• MuJoCo simulation
• reinforcement learning