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Of rats and robots: A mutual learning paradigm
Oguzcan Nas1, Defne Albayrak1, Gunes Unal1
1Behavioral Neuroscience Laboratory, Department of Psychology, Boğaziçi University, Istanbul, Turkey.
Journal of the Experimental Analysis of Behavior
|March 12, 2025
Summary
This study introduces a new mutual learning system where animals and robots train each other. This bidirectional approach allows both partners to adapt and achieve goals through rewarding or punishing signals.
Area of Science:
- Robotics
- Artificial Intelligence
- Animal Behavior
Background:
- Traditional animal training and AI research often involve unidirectional learning.
- Existing paradigms lack mutual adaptation between biological and artificial agents.
Purpose of the Study:
- To develop and validate a novel animal-robot interaction paradigm for bidirectional learning.
- To enable dynamic, goal-oriented cooperation between Wistar rats and robots.
Main Methods:
- Implemented a mutual learning framework where agents provide positive/negative feedback.
- Tested the paradigm in silico using artificial reinforcement learning agents.
- Validated the system in vivo with rat-robot pairs in operant conditioning tasks.
Main Results:
- Both artificial agents and Wistar rats demonstrated mutual learning, adapting behavior for reward maximization.
- Rats rapidly learned reward-associated behaviors and showed avoidance learning.
- The system effectively facilitated adaptive interactions based on partner feedback.
Conclusions:
- The developed paradigm enables effective bidirectional learning between animals and robots.
- This framework advances animal-machine interaction research and the study of learning rules.
- Offers a versatile platform for exploring cooperative intelligence in diverse settings.
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