Bio-Inspired Plastic Neural Networks for Zero-Shot Out-of-Distribution Generalization in Complex Animal-Inspired Robots

Abstract-- Artificial neural networks can be used to solve a variety of robotic tasks. However, they risk failing catastrophically when faced with out-of-distribution (OOD) situations. Several approaches have employed a type of synaptic plasticity known as Hebbian learning that can dynamically adjust weights based on local neural activities. Research has shown that synaptic plasticity can make policies more robust and help them adapt to unforeseen changes in the environment. In this work, we improve the Hebbian network with a weight normalization mechanism for preventing weight divergence, analyze the principal components of the Hebbian's weights, The disadvantages of these In the field of machine learning research, deep neural types of solutions are that they extend the necessary training networks (DNNs) have been shown to be useful across a time or risk, resulting in an architecture that is overly specific wide range of tasks [1], [2], including robotics [3], [4], [5]. to the task for which it was designed [11], [12]. However, policies for agent control based on deep neural Animals, on the other hand, demonstrate remarkable networks tend to be brittle [6], meaning that they are at risk adaptability in adjusting their motor patterns to accomplish of catastrophic failure when faced with out-of-distribution various tasks. Synaptic plasticity is thought to play (OOD) situations [7], [8].