Sim2Real for Soft Robotic Fish via Differentiable Simulation

TL;DR

This paper introduces a differentiable simulation framework for soft robotic fish that accurately predicts dynamic behavior and learns material parameters, enabling improved design and control of soft robots.

Contribution

The work presents a novel differentiable simulation tool that learns material properties and predicts soft robotic fish dynamics with high accuracy, adaptable to various geometries.

Findings

Accurately predicts soft robotic fish dynamics within 3% error.

Learns physically plausible Young's moduli for different elastomers.

Compatible with varying internal actuator geometries.

Abstract

Accurate simulation of soft mechanisms under dynamic actuation is critical for the design of soft robots. We address this gap with our differentiable simulation tool by learning the material parameters of our soft robotic fish. On the example of a soft robotic fish, we demonstrate an experimentally-verified, fast optimization pipeline for learning the material parameters from quasi-static data via differentiable simulation and apply it to the prediction of dynamic performance. Our method identifies physically plausible Young's moduli for various soft silicone elastomers and stiff acetal copolymers used in creation of our three different robotic fish tail designs. We show that our method is compatible with varying internal geometry of the actuators, such as the number of hollow cavities. Our framework allows high fidelity prediction of dynamic behavior for composite bi-morph bending structures in real hardware to millimeter-accuracy and within 3 percent error normalized to actuator length. We provide a differentiable and robust estimate of the thrust force using a neural network thrust predictor; this estimate allows for accurate modeling of our experimental setup measuring bollard pull. This work presents a prototypical hardware and simulation problem solved using our differentiable framework; the framework can be applied to higher dimensional parameter inference, learning control policies, and computational design due to its differentiable character.