Elastic Locomotion with Mixed Second-order Differentiation

TL;DR

This paper introduces a novel elastic locomotion framework that uses a mixed second-order differentiation algorithm combining analytic and numerical methods, enabling efficient inverse simulation for complex soft body movements.

Contribution

It presents a new second-order differentiation scheme combining complex-step finite difference with automatic differentiation for elastic locomotion simulation.

Findings

Enables direct use of Newton's method for elastic locomotion.

Demonstrates diverse soft body locomotion behaviors.

Achieves strong second-order convergence in inverse simulation.

Abstract

We present a framework of elastic locomotion, which allows users to enliven an elastic body to produce interesting locomotion by prescribing its high-level kinematics. We formulate this problem as an inverse simulation problem and seek the optimal muscle activations to drive the body to complete the desired actions. We employ the interior-point method to model wide-area contacts between the body and the environment with logarithmic barrier penalties. The core of our framework is a mixed second-order differentiation algorithm. By combining both analytic differentiation and numerical differentiation modalities, a general-purpose second-order differentiation scheme is made possible. Specifically, we augment complex-step finite difference (CSFD) with reverse automatic differentiation (AD). We treat AD as a generic function, mapping a computing procedure to its derivative w.r.t. output loss, and promote CSFD along the AD computation. To this end, we carefully implement all the arithmetics used in elastic locomotion, from elementary functions to linear algebra and matrix operation for CSFD promotion. With this novel differentiation tool, elastic locomotion can directly exploit Newton's method and use its strong second-order convergence to find the needed activations at muscle fibers. This is not possible with existing first-order inverse or differentiable simulation techniques. We showcase a wide range of interesting locomotions of soft bodies and creatures to validate our method.