  Learning to walk -- i.e., learning locomotion under performance and energy
constraints continues to be a challenge in legged robotics. Methods such as
stochastic gradient, deep reinforcement learning (RL) have been explored for
bipeds, quadrupeds and hexapods. These techniques are computationally intensive
and often prohibitive for edge applications. These methods rely on complex
sensors and pre-processing of data, which further increases energy and latency.
Recent advances in spiking neural networks (SNNs) promise a significant
reduction in computing owing to the sparse firing of neuros and has been shown
to integrate reinforcement learning mechanisms with biologically observed spike
time dependent plasticity (STDP). However, training a legged robot to walk by
learning the synchronization patterns of central pattern generators (CPG) in an
SNN framework has not been shown. This can marry the efficiency of SNNs with
synchronized locomotion of CPG based systems providing breakthrough end-to-end
learning in mobile robotics. In this paper, we propose a reinforcement based
stochastic weight update technique for training a spiking CPG. The whole system
is implemented on a lightweight raspberry pi platform with integrated sensors,
thus opening up exciting new possibilities.
