Bootstrapped Neuro-Simulation for Damage Recovery in Complex Robots

Bootstrapped Neuro-Simulation is a technique in which a Neural Network Simulator, used for the evaluation of controllers during the Evolutionary Robotics process, is trained concurrently with the evolution of the controllers themselves. This removes the need for creating a simulator before commencin...

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Bibliographic Details
Published in2022 IEEE Congress on Evolutionary Computation (CEC) pp. 1 - 8
Main Authors Leonard, Brydon A., Du Plessis, Mathys C.
Format Conference Proceeding
LanguageEnglish
Published IEEE 18.07.2022
Subjects
Adaptation models
damage recovery
Evolutionary robotics
Legged locomotion
Neural networks
neuro-simulation
Process control
Training
Training data
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Summary:Bootstrapped Neuro-Simulation is a technique in which a Neural Network Simulator, used for the evaluation of controllers during the Evolutionary Robotics process, is trained concurrently with the evolution of the controllers themselves. This removes the need for creating a simulator before commencing controller evolution and results in a simulator that is not only tailored specifically to the robot being used, but also to the task that the robot is expected to perform. This paper demonstrates that Bootstrapped Neuro-Simulation can also be used for damage recovery since the Neural Network Simulator adapts to physical changes to the robot and enables the evolution of controllers that utilize the undamaged components of the robot. Limbs of a hexapod robot are disabled to simulate damage in the experiments described in this paper. Various adaptations to the Bootstrapped Neuro-Simulation algorithm are investigated in simulation. A real-world robot is used to demonstrate the successful recovery from damage and to illustrate situations where the adaptations were found to be beneficial.
DOI:10.1109/CEC55065.2022.9870204