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Updated: Mar 8, 2026
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Experimental Methods to Study Human Postural Control
Published on: September 11, 2019
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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.
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.
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