Soft Passive Swimmer Optimization: From Simulation to Reality Using Data-Driven Transformation

Soft, bio-inspired robotic swimmers are showing significant potential in terms of efficient motion and maneu-verability. However, modeling and understanding the fluid-structure interactions between compliant soft bodies and water is highly complex, limiting our ability to optimize or understand the effect of both design and controller parameters. We propose combining simulation of soft passive swimming structures with experimental data to improve the accuracy of the simulation by identifying the transformation which best maps the simulation to this experimental data. This allows us to leverage the physics that are captured by the simulations whilst closing the sim-to-real gap by using a limited amount of experimental data. We use this approach to model a simple, modular robotic system based upon passive tentacles and show how we can close the reality gap and optimize the design of the tentacle structures. This optimized structure is used to create a full robot swimmer, where the optimized tentacles can be used to create a robot with a swimming speed of 6.2 cm/s, and can introduce a rudder like ‘deflector’ to also control the direction of the robot's motion.