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Self-Learning Event Mistiming Detector Based on Central Pattern Generator.

Rudolf Szadkowski¹, Miloš Prágr¹, Jan Faigl¹

¹Computational Robotics Laboratory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czechia.

Frontiers in Neurorobotics

|February 22, 2021

Summary

This summary is machine-generated.

This study introduces a novel self-supervised method for mistiming detection in legged robots, using a Central Pattern Generator (CPG) to enhance gait control and enable obstacle avoidance and foothold adjustment.

Keywords:

Hebbian learning bio-inspired robotics central pattern generator hexapod walking robot locomotion phase estimation radial basis function neuron reflexes

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Cross-Modal Multivariate Pattern Analysis

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Published on: November 9, 2011

Area of Science:

Robotics
Computational Neuroscience
Biomimetic Engineering

Background:

Central Pattern Generators (CPGs) control rhythmic movements in animals and are increasingly used in biomimetic robots for locomotion.
CPGs can function as rhythmic signal generators and sensory phase estimators, but their direct application in phase estimation for irregularity detection is underexplored.

Purpose of the Study:

To investigate the utilization of CPGs for detecting phase irregularities in sensory input during robotic locomotion.
To propose and validate a novel self-supervised learning method for mistiming detection in legged robots.

Main Methods:

A self-supervised neural detector was developed, comprising a CPG for phase estimation, a Radial Basis Function neuron for sensory event anticipation, and a Leaky Integrate-and-Fire neuron for mistiming detection.
The detector was integrated with a CPG-based gait controller, enabling reflexes for obstacle avoidance and foothold adjustment.
The system was trained and tested on a hexapod walking robot in laboratory and real-world subterranean environments.

Main Results:

The proposed mistiming detection system successfully triggered elevator and search reflexes in response to disturbed sensory event timing.
The hexapod robot demonstrated effective navigation in unstructured and slippery environments by utilizing the mistiming detection system.
The self-supervised learning approach proved effective for training the neural detector during robot locomotion.

Conclusions:

The study successfully demonstrates the efficacy of CPG-based mistiming detection for enhancing robotic locomotion and environmental negotiation.
The developed self-supervised learning method offers a promising approach for robust gait control in dynamic and unpredictable environments.
The system's successful deployment in real-world field tests validates its practical applicability for autonomous robots.