Simple Muscle Models Regularize Motion in a Robotic Leg with Neurally-Based Step Generation

Abstract

Robotic control systems inspired by animals are enticing to the robot designer due to their promises of simplicity, elegance and robustness. While there has been success in applying general and behaviorally-based knowledge of biological systems to control, we are investigating the use of control based on known and hypothesized neural pathways in specific model animals. Neural motor systems in animals are only meaningful in the context of their mechanical body, and the behavior of the system can be highly dependent on nonlinear and dynamic properties of the mechanical part of the system. It is therefore reasonable to believe that to reproduce behavior, the physical characteristics of the biological system must also be modeled or accounted for. In this paper we examine the performance of a robotic system with three types of muscle model: null, piecewise-constant, and linear. Results show that adding very simple models of muscle properties at a single joint cause marked improvement in the performance of a neurally-based step generator for a 3-degree-of-freedom robotic leg.