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. To the best of our knowledge, this is the first work to implement SNN in legged robots.

翻译：近年来，基于深度强化学习的腿足机器人取得了显著进展。四足机器人已展现出在复杂环境中完成挑战性任务的能力，并被部署于真实场景以辅助人类。与此同时，双足及人形机器人在多种高难度任务中实现了突破。当前强化学习方法可利用多样化的机器人本体与历史信息来执行动作。然而，以往研究并未强调网络推理的速度和能耗，也未重视神经网络本身的生物学意义。所采用的网络多为基于多层感知机（MLP）的传统人工神经网络。本文成功将新型脉冲神经网络（SNN）应用于腿足机器人处理任务，在多种模拟地形中取得了卓越效果。SNN在推理速度和能耗方面相比传统神经网络具有天然优势，其对本体感知信号的脉冲形式处理亦具有更好的生物可解释性。据我们所知，这是首次在腿足机器人中实现SNN的开创性工作。