Evolving Hebbian Learning Rules in Voxel-Based Soft Robots

Andrea Ferigo

doi:10.1109/TCDS.2022.3226556

Evolving Hebbian Learning Rules in Voxel-Based Soft Robots

2023-09-01

2

OA

AI

DOI：10.1109/TCDS.2022.3226556

Original

Original request for help

Save

Abstract

En 中文

According to Hebbian theory, synaptic plasticity is the ability of neurons to strengthen or weaken the synapses among them in response to stimuli. It plays a fundamental role in the processes of learning and memory of biological neural networks. With plasticity, biological agents can adapt on multiple timescales and outclass artificial agents, the majority of which still rely on static artificial neural network (ANN) controllers. In this work, we focus on voxel-based soft robots ( VSRs), a class of simulated artificial agents, composed as aggregations of elastic cubic blocks. We propose a Hebbian ANN controller where every synapse is associated with a Hebbian rule that controls the way the weight is adapted during the VSR lifetime. For a given task and morphology, we optimize the controller for the task of locomotion by evolving, rather than the weights, the parameters of Hebbian rules. Our results show that the Hebbian controller is comparable, often better than a non-Hebbian baseline and that it is more adaptable to damages. We also provide novel insights into the inner workings of plasticity and demonstrate that true learning does take place, as the evolved controllers improve over the lifetime and generalize well.

Keywords:

Evolutionary robotics

Hebbian learning

synaptic plasticity

voxel-based soft robots (VSRs)

Evolving Hebbian Learning Rules in Voxel-Based Soft Robots

Abstract

Journal

Organization