  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.
