Walking with perturbations: a guide for biped humans and robots

Abstract

This paper provides an update on the neural control of bipedal walking in relation to bioinspired models and robots. It is argued that most current models or robots are based on the construct of a symmetrical central pattern generator (CPG). However, new evidence suggests that CPG functioning is basically asymmetrical with its flexor half linked more tightly to the rhythm generator. The stability of bipedal gait, which is an important problem for robots and biological systems, is also addressed. While it is not possible to determine how biological biped systems guarantee stability, robot solutions can be useful to propose new hypotheses for biology. In the second part of this review, the focus is on gait perturbations, which is an important topic in robotics in view of the frequent falls of robots when faced with perturbations. From the human physiology it is known that the initial reaction often consists of a brief interruption followed by an adequate response. For instance, the successful recovery from a trip is achieved using some basic reactions (termed elevating and lowering strategies), that depend on the phase of the step cycle of the trip occurrence. Reactions to stepping unexpectedly in a hole depend on comparing expected and real feedback. Implementation of these ideas in models and robotics starts to emerge, with the most advanced robots being able to learn how to fall safely and how to deal with complicated disturbances such as provided by walking on a split-belt.

Effects of supraspinal feedback on human gait: rhythmic auditory distortion

“…There was clear evidence that young healthy subjects adapt to the auditory metronome beat during gait. This underscores the influence of supraspinal inputs on the purported Central Pattern Generators (CPG) of gait [33] supporting the role of a cortical loop in a hypothetical gait CPG as has been proposed by others [34][35][36][37].…”

Learning an Efficient Gait Cycle of a Biped Robot Based on Reinforcement Learning and Artificial Neural Networks

“…The computation time reached in this work is around 0.0014 ms. Other works take further the idea of comparing with human locomotion by restricting certain joints and observing the effects and torque changes in the robot [27]. Symmetricity of gait cycle patterns is analyzed in detail in Reference [28]. A thorough survey on stability criteria in biped robots is presented in Reference [28] as well.…”

“…Symmetricity of gait cycle patterns is analyzed in detail in Reference [28]. A thorough survey on stability criteria in biped robots is presented in Reference [28] as well.…”

Predicted Step Viability: a stability criterion for biped gait

“…The development of biped walking robots is one of the most challenging quests in the field of robotics [6]. A general controller architecture of the walker needs an agent to control stability ( Fig.…”

“…One of the major problems in biped gait is the definition of a stability criterion that can be used to generate trajectories that prevent the robot from falling during locomotion. The problem of finding a stability criterion begins with the question of defining stability in biped gait [6]. In this context, an operational definition of stability can be based on the ability of the robot to avoid falling.…”