Differentiable Simulation of Soft Robots with Frictional Contacts

TL;DR

This paper introduces a unified differentiable simulation framework for soft robots that accurately models contact and friction, enabling faster control optimization and better design through gradient-based methods.

Contribution

It presents a novel method for computing derivatives in soft robot simulations with contact and friction, improving efficiency and accuracy over existing approaches.

Findings

Effective in controlling soft systems

Handles nonsmooth contact dynamics

Leads to faster optimization convergence

Abstract

In recent years, soft robotics simulators have evolved to offer various functionalities, including the simulation of different material types (e.g., elastic, hyper-elastic) and actuation methods (e.g., pneumatic, cable-driven, servomotor). These simulators also provide tools for various tasks, such as calibration, design, and control. However, efficiently and accurately computing derivatives within these simulators remains a challenge, particularly in the presence of physical contact interactions. Incorporating these derivatives can, for instance, significantly improve the convergence speed of control methods like reinforcement learning and trajectory optimization, enable gradient-based techniques for design, or facilitate end-to-end machine-learning approaches for model reduction. This paper addresses these challenges by introducing a unified method for computing the derivatives of mechanical equations within the finite element method framework, including contact interactions modeled as a nonlinear complementarity problem. The proposed approach handles both collision and friction phases, accounts for their nonsmooth dynamics, and leverages the sparsity introduced by mesh-based models. Its effectiveness is demonstrated through several examples of controlling and calibrating soft systems.