Regulating Speed in a Neuromuscular Human Running Model

Abstract

A versatile model of human locomotion control can have large impact on the robotics community by eliciting new control ideas for legged robots and providing simulation test-beds for walking assistive robots. There exist neural control models that can generate human-like diverse and robust locomotion behaviors. However, most of the locomotion behaviors have been generated by exploring different sets of low-level control parameters. In this study, we incorporate a higher-layer speed adaptation policy to a previously proposed neuromuscular human model that enables the model to run at speeds ranging from 2.4 to 4.0 ms−1 by changing only a single command of the target velocity. In addition, we investigate simple strategies that facilitate speed changes. Among the strategies we explore, modulating the trunk lean shows fast and reliable acceleration and deceleration in average of 0.35 and -0.37 ms−2, respectively.