Online learning for the legged robot locomotion under performance and energy constraints remains to be a challenge. Methods such as stochastic gradient, deep reinforcement learning (RL) have been explored for bipeds, quadrupeds and hexapods. These techniques are computationally intensive and thus difficult to implement on edge computing platforms. These methods are also inefficient in energy consumption and throughput because of their reliance on complex sensors and pre-processing of data. On the other hand, neuromorphic computing paradigms, such as spiking neural networks (SNN), become increasingly favorable in low power computing on edge intelligence. SNN has exhibited the capability of performing reinforcement learning mechanisms with biomimetic spike time-dependent plasticity (STDP) of synapses. However, training a legged robot to walk in the synchronized gait patterns generated by a central pattern generator (CPG) in an SNN framework has not yet been explored. Such a method can combine the efficiency of SNNs with the synchronized locomotion of CPG based systems – providing breakthrough performance improvement of end-to-end learning in mobile robotics. In this paper, we propose a reinforcement based stochastic learning technique for training a spiking CPG for a hexapod robot which learns to walk using bio-inspired tripod gait without prior knowledge. The whole system is implemented on a lightweight raspberry pi platform with integrated sensors. Our method opens new opportunities for online learning with limited edge computing resources.

中文翻译：

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学习走路：通过基于强化的尖峰中央模式生成的仿生六足动物运动

在性能和能量限制下腿式机器人运动的在线学习仍然是一个挑战。已经为两足动物、四足动物和六足动物探索了诸如随机梯度、深度强化学习 (RL) 等方法。这些技术是计算密集型的，因此难以在边缘计算平台上实施。由于依赖复杂的传感器和数据预处理，这些方法在能耗和吞吐量方面也效率低下。另一方面，神经形态计算范式，例如尖峰神经网络（SNN），在边缘智能的低功耗计算中变得越来越有利。SNN 展示了利用突触的仿生尖峰时间依赖性可塑性 (STDP) 执行强化学习机制的能力。然而，尚未探索训练有腿机器人以 SNN 框架中的中央模式生成器 (CPG) 生成的同步步态模式行走。这种方法可以将 SNN 的效率与基于 CPG 的系统的同步运动相结合——为移动机器人的端到端学习提供突破性的性能改进。在本文中，我们提出了一种基于强化的随机学习技术，用于训练六足机器人的尖峰 CPG，该机器人在没有先验知识的情况下学习使用仿生三脚架步态行走。整个系统是在一个带有集成传感器的轻量级树莓派平台上实现的。我们的方法为边缘计算资源有限的在线学习开辟了新的机会。