Online Reward-Based Training of Spiking Central Pattern Generator for Hexapod Locomotion

Online learning in legged robot under stringent performance and energy constraints thwarts the application of conventional reinforcement learning and optimization algorithms. The integration of complex sensors and data pre-processing required in using these algorithms makes this more challenging. Spiking neural networks allow local learning and low computing power opening new possibilities neuromorphic paradigm to such tasks. Central pattern generation based learning to walk in hexapod robots perfectly matches the temporal learning in SNNs allowing end-to-end learning. We propose a stochastic reinforcement-based algorithm allowing the hexapod to learn using the reward generated by the gyro sensors and camera-based visual inputs. The system is implemented on a Raspberry pi to demonstrate convergence to bio-observed gait patterns.