Self-Learning Event Mistiming Detector Based on Central Pattern Generator

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00347943" target="_blank" >RIV/68407700:21230/21:00347943 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.3389/fnbot.2021.629652" target="_blank" >https://doi.org/10.3389/fnbot.2021.629652</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3389/fnbot.2021.629652" target="_blank" >10.3389/fnbot.2021.629652</a>

Result language

angličtina

Original language name

Self-Learning Event Mistiming Detector Based on Central Pattern Generator

Original language description

A repetitive movement pattern of many animals, a gait, is controlled by the Central Pattern Generator (CPG), providing rhythmic control synchronous to the sensed environment. As a rhythmic signal generator, the CPG can control the motion phase of biomimetic legged robots without feedback. The CPG can also act in sensory synchronization, where it can be utilized as a sensory phase estimator. Direct use of the CPG as the estimator is not common, and there is little research done on its utilization in the phase estimation. Generally, the sensory estimation augments the sensory feedback information, and motion irregularities can reveal from comparing measurements with the estimation. In this work, we study the CPG in the context of phase irregularity detection, where the timing of sensory events is disturbed. We propose a novel self-supervised method for learning mistiming detection, where the neural detector is trained by dynamic Hebbian-like rules during the robot walking. The proposed detector is composed of three neural components: (i) the CPG providing phase estimation, (ii) Radial Basis Function neuron anticipating the sensory event, and (iii) Leaky Integrate-and-Fire neuron detecting the sensory mistiming. The detector is integrated with the CPG-based gait controller. The mistiming detection triggers two reflexes: the elevator reflex, which avoids an obstacle, and the search reflex, which grasps a missing foothold. The proposed controller is deployed and trained on a hexapod walking robot to demonstrate the mistiming detection in real locomotion. The trained system has been examined in the controlled laboratory experiment and real field deployment in the Bull Rock cave system, where the robot utilized mistiming detection to negotiate the unstructured and slippery subterranean environment.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA18-18858S" target="_blank" >GA18-18858S: Robotic Lifelong Learning of Multi-legged Robot Locomotion Control in Autonomous Data Collection Missions</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Frontiers in Neurorobotics

ISSN

1662-5218

e-ISSN

1662-5218

Volume of the periodical

15

Issue of the periodical within the volume

February

Country of publishing house

CH - SWITZERLAND

Number of pages

14

Pages from-to

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UT code for WoS article

000619037300001

EID of the result in the Scopus database

2-s2.0-85101132990