  In recent years, legged robots based on deep reinforcement learning have made
remarkable progress. Quadruped robots have demonstrated the ability to complete
challenging tasks in complex environments and have been deployed in real-world
scenarios to assist humans. Simultaneously, bipedal and humanoid robots have
achieved breakthroughs in various demanding tasks. Current reinforcement
learning methods can utilize diverse robot bodies and historical information to
perform actions. However, prior research has not emphasized the speed and
energy consumption of network inference, as well as the biological significance
of the neural networks themselves. Most of the networks employed are
traditional artificial neural networks that utilize multilayer perceptrons
(MLP). In this paper, we successfully apply a novel Spiking Neural Network
(SNN) to process legged robots, achieving outstanding results across a range of
simulated terrains. SNN holds a natural advantage over traditional neural
networks in terms of inference speed and energy consumption, and their
pulse-form processing of body perception signals offers improved biological
interpretability. Applying more biomimetic neural networks to legged robots can
further reduce the heat dissipation and structural burden caused by the high
power consumption of neural networks. To the best of our knowledge, this is the
first work to implement SNN in legged robots.
