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。以大鼠机器人NeRmo作为实验平台。由于体积受限，小型四足机器人通常配备的传感器数量少且性能弱，导致难以精准感知和响应环境变化。在此背景下，传感器反馈数据不足且不精确，使得基于强化学习的自适应运动生成面临挑战。为克服这些难题，本文提出一种新型强化学习方法，通过提取有效感知信息来增强小型四足机器人的环境适应能力。根据机器人步态步频，利用傅里叶变换结果导出的正弦函数分析传感器数据的关键信息。同时，提出一种多功能奖励机制，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种环境下生成大鼠机器人运动行为的有效性。实验结果表明，该方法能够使大鼠机器人在斜坡、楼梯及螺旋楼梯等不同地形上保持稳定运动。