Learning Whole-Body Control for Small-Sized Quadruped Robots with a Flexible Spine

Abstract

Improving the adaptability of small-sized quadruped robots has been a longstanding challenge in robotics. However, the weak whole-body coordination in existing small-sized quadruped robots limits their locomotion in many environments. In this work, we propose a teacher-student online learning framework for agile whole-body control of small-sized quadruped robots with a flexible spine. We first select a simple and effective gait pattern, the diagonal symmetrical sequence, using a dynamics model. Based on the reference motions provided by the gait pattern and combined with privileged information, we train a teacher policy to generate high-quality motion data. After setting the state space to match the actual robot’s state space, we initialize the robot’s initial state using the teacher data and train a student policy. Finally, we deploy the student policy on the SQuRo-Lite, a small-sized quadruped robot with a flexible spine, demonstrating that our approach can achieve stable yet dynamic locomotion for walking and turning. In the variable-spacing slalom experiment, the robot is able to flexibly adjust the motion patterns of its spine and legs based on commands, enabling dynamic changes in its turning radius. This further validates that our approach can achieve agile whole-body control for small-sized quadruped robots. This work helps broaden the application scenarios of small quadruped robots.