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Related Experiment Video

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Experimental Methods to Study Human Postural Control

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Published on: September 11, 2019

Evolution of Joint-Level Control for Quadrupedal Locomotion.

Jared M Moore¹, Philip K McKinley²

¹Grand Valley State University.

Artificial Life

|February 1, 2017

Summary

This study introduces a hierarchical robot control system using artificial neural networks (ANNs) and digital muscle models (DMMs). The ANN-DMM approach enhances robot control and simplifies ANN complexity compared to ANN-only systems.

Keywords:

Evolutionary robotics artificial neural network digital muscle joint-level control morphology

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Area of Science:

Robotics
Artificial Intelligence
Biomimicry

Background:

Animal musculoskeletal systems exhibit sophisticated joint-level control.
Current artificial systems often rely on high-level controllers, neglecting joint-level mechanics.

Purpose of the Study:

To investigate a hierarchical robot control approach inspired by animal joint-level control.
To compare the performance of artificial systems using combined artificial neural network (ANN) and digital muscle model (DMM) controllers against ANN-only and DMM-only controllers.

Main Methods:

Developed a hierarchical control system integrating an ANN with DMM-based joint controllers.
Evolved artificial organisms (animats) with ANN-DMM controllers.
Compared performance metrics of evolved animats across different control strategies.

Main Results:

ANN-DMM controllers outperformed ANN-only controllers in performance.
The ANN-DMM approach resulted in less complex ANNs (fewer neurons and connections).
Concurrent evolution of morphology and control in DMMs emulated natural musculoskeletal systems.

Conclusions:

Hierarchical control integrating joint-level mechanisms (DMMs) offers superior performance and efficiency in artificial systems.
This biomimetic approach allows for evolved artificial systems with decentralized, joint-level control aspects, mirroring natural organisms.