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Robot Programming from Fish Demonstrations.

Claudio Massimo Coppola¹, James Bradley Strong¹, Lissa O'Reilly²

¹Department of Computer Science, Aberystwyth University, Ceredigion SY23 3DB, UK.

Biomimetics (Basel, Switzerland)

|June 27, 2023

Summary

This summary is machine-generated.

This study introduces a fish-inspired robot control framework using learning from demonstration. Fish-inspired controllers achieved over 96% success in navigation tasks, outperforming manual controllers.

Keywords:

ANN controller bio-inspired robotics fuzzy controller learning from demonstration robot programming

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Area of Science:

Robotics
Bio-inspired AI
Animal Behavior

Background:

Robots require enhanced autonomy and adaptability.
Fish exhibit complex environmental learning capabilities.

Purpose of the Study:

Develop a novel learning from demonstration framework for fish-inspired robot control.
Minimize human intervention in generating robot control programs.

Main Methods:

Framework with six modules: demonstration, tracking, trajectory analysis, data acquisition, controller generation, and evaluation.
Artificial neural network for automatic fish tracking (85% detection rate, <0.04 body length error).
Case study on cue-based navigation using 2D particle simulations.

Main Results:

Generated fish-inspired controllers achieved >96% success rate in navigation tasks, exceeding benchmarks by 3%.
One controller demonstrated >98% generalization performance from random initial conditions, outperforming benchmarks by 12%.
Fish tracking network achieved 85% detection accuracy and low pose estimation error.

Conclusions:

The framework effectively generates high-performing, adaptable robot controllers inspired by fish behavior.
This approach serves as a research tool for understanding fish navigation and advancing bio-inspired robotics.
Demonstrates the potential of leveraging animal intelligence for robotic applications.