A Data-Driven Approach to Connection Modeling

TL;DR

This paper introduces a data-driven framework for estimating local connection models of animal and robot gait cycles from motion capture data, enabling analysis of cyclic locomotion in complex systems with noise.

Contribution

It presents a novel method to estimate local connection models directly from noisy motion data, reducing the need for exhaustive experiments in systems with multiple degrees of freedom.

Findings

Effective model estimation under noisy conditions

Validated approach with simulated serpentine swimmers

Potential for application to real biological and robotic systems

Abstract

The study of motion in animals and robots has been aided by insights from geometric mechanics. In friction dominated systems, a mechanical "connection" can provide a high fidelity mechanical model. The connection is a co-vector (Lie algebra) valued map on the configuration space of the system. As such, empirically estimating a global model of the connection requires a truly exhaustive collection of experiments, and is thus prohibitive on all systems with even a moderate number of degrees of freedom. In this work, insights from data driven oscillator theory enable us to define a framework for estimating a local model of a connection in the vicinity of observed animal and robot gait cycles. The estimates are produced directly from motion capture data of a stochastically perturbed cyclic behavior. We demonstrate the model extraction process under noisy, experiment-like conditions by simulating planar multi-segment serpentine swimmers in a low Reynolds number (viscous-friction) environment. Following this, we assess model accuracy in the presence of observation error. Validating our method's capability to produce accurate models in the presence of simulated system and observational noise motivates its usage on real robotic and biological systems.