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

Mike Y. Michelis, Nana Obayashi, Josie Hughes, Robert K. Katzschmann

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.

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

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.
Paper Structure (14 sections, 4 equations, 8 figures, 1 table)

This paper contains 14 sections, 4 equations, 8 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Experimental Setup
  4. Hardware Setup
  5. Simulation Setup
  6. System Identification
  7. Tail Stiffness
  8. Tail Actuation
  9. Fluid Coefficients
  10. Comparison to EBT Baseline
  11. Results
  12. Sim-to-Real for Forward Swimming
  13. Reinforcement Learning for Target Tracking
  14. Conclusion and Future Work

Figures (8)

  • Figure 1: Overview of the simulated and real swimmer robots. A) Robot swimming in the pool captured from a top-down view, images overlaid are 3s apart. B) The 11 markers are tracked and extracted from the video. C) Side-view of the hardware with a single motor actuating the tendon-driven tail. D) Simulated robot with markers indicated by small orange spheres. Center of mass is located at the large orange sphere in the head. E) Simulated trajectory after matching fluid model to real experiment.
  • Figure 2: Overview of the main mechanisms for the swimmer simulation environment. We simplify the deformable spine with multiple hinge joints as an articulated rigid body, we use stiff elastic tendons for the tendon-driven actuation, a velocity-controlled motor that pulls the tendons, and a simplified fluid model to mimic real-world swimming behavior.
  • Figure 3: Natural frequency in both time and frequency domain of the fish tail measured on the real robot tail (left) and of the simulated system after matching the main amplitude frequency with reality (right). The different colors in the left plot indicate the 5 spine segments that are tracked during the oscillation.
  • Figure 4: System identification of motor angular frequency and phase offset to minimize the distance between simulated and real markers, with a minimal distance of 0.026m for the 0.60Hz swimming shown on the left.
  • Figure 5: Optimized results of sim-to-real in-water experiment for 1.19Hz data. We show the tail fin marker position (left) and forward swimming position (right) to validate the thrust generated by the hydrodynamics model. The average distance error on all markers is computed to be 0.016m.
  • ...and 3 more figures