This research focuses on developing reinforcement learning approaches for the locomotion generation of small-size quadruped robots. The rat robot NeRmo is employed as the experimental platform. Due to the constrained volume, small-size quadruped robots typically possess fewer and weaker sensors, resulting in difficulty in accurately perceiving and responding to environmental changes. In this context, insufficient and imprecise feedback data from sensors makes it difficult to generate adaptive locomotion based on reinforcement learning. To overcome these challenges, this paper proposes a novel reinforcement learning approach that focuses on extracting effective perceptual information to enhance the environmental adaptability of small-size quadruped robots. According to the frequency of a robot's gait stride, key information of sensor data is analyzed utilizing sinusoidal functions derived from Fourier transform results. Additionally, a multifunctional reward mechanism is proposed to generate adaptive locomotion in different tasks. Extensive simulations are conducted to assess the effectiveness of the proposed reinforcement learning approach in generating rat robot locomotion in various environments. The experiment results illustrate the capability of the proposed approach to maintain stable locomotion of a rat robot across different terrains, including ramps, stairs, and spiral stairs.

翻译：本研究聚焦于开发用于小型四足机器人运动生成的强化学习方法，以鼠形机器人NeRmo为实验平台。由于体积受限，小型四足机器人的传感器通常数量较少且性能较弱，导致难以准确感知并响应环境变化。在此背景下，传感器反馈数据的不足与不精确使得基于强化学习的自适应运动生成面临困难。针对上述挑战，本文提出了一种新型强化学习方法，该方法专注于提取有效感知信息以增强小型四足机器人的环境适应性。通过机器人步态步频，利用傅里叶变换结果推导的正弦函数分析传感器数据的关键信息，并设计多功能奖励机制以在不同任务中生成自适应运动。通过大量仿真实验评估所提强化学习方法在多环境中生成鼠机器人运动的效果，结果表明该方法能使鼠机器人在斜坡、楼梯及螺旋楼梯等不同地形上维持稳定运动。