Bio-inspired neural networks with central pattern generators for learning multi-skill locomotion

Biological neural circuits, central pattern generators (CPGs), located at the spinal cord are the underlying mechanisms that play a crucial role in generating rhythmic locomotion patterns. In this paper, we propose a novel approach that leverages the inherent rhythmicity of CPGs to enhance the locomotion capabilities of quadruped robots. Our proposed network architecture incorporates CPGs for rhythmic pattern generation and a multi-layer perceptron (MLP) network for fusing multi-dimensional sensory feedback. In particular, we also proposed a method to reformulate CPGs into a fully-differentiable, stateless network, allowing CPGs and MLP to be jointly trained using gradient-based learning. The effectiveness and performance of our approach are demonstrated through extensive experiments. Our learned locomotion policies exhibit agile and dynamic locomotion behaviors which are capable of traversing over uneven terrain blindly and resisting external perturbations. Furthermore, results demonstrated the remarkable multi-skill capability within a single unified policy network, including fall recovery and various quadrupedal gaits. Our study highlights the advantages of integrating bio-inspired neural networks which are capable of achieving intrinsic rhythmicity and fusing sensory feedback for generating smooth, versatile, and robust locomotion behaviors, including both rhythmic and non-rhythmic locomotion skills.