Neural Hebbain plastic control network for adaptive locomotion

Neural synaptic plasticity is fundamental to adaptive capabilities in animals. Inspired by this, researchers have developed robotic controllers with plastic neural networks, employing Hebbian learning rules to achieve adaptive locomotion. Najarro demonstrated that a neural network with Hebbian learning can adapt to morphological damage in a simulated quadrupedal robot, even when the damage was not seen during training. Although the Hebbian network enables adaptation to unseen situations in locomotion tasks, it remains unclear whether it learns high-level inter-leg coordination or merely encode specific joint trajectories for adaptation. To address this question, this study investigates Hebbian network by systematically shuffling the legs between the left and right sides, analyzing their impact on locomotor adaptation.