Learning dynamically stable Ultra-Slow Gait in Quadruped Robots
This research proposes a novel control architecture for dynamically stable ultra-slow quadruped locomotion on unstructured and slippery terrains. The approach is based on Imitation Learning (IL), where two neural controllers are trained to imitate an optimal Quadratic Programming (QP) controller derived from a Linear Time Invariant (LTI) Variation-Based Linearized (VBL) model of the robot. Stability and safety are incorporated through Lyapunov-inspired and Linear Matrix Inequality (LMI) constraints integrated into the training process. A switching strategy between leg pairs enables continuous balance during slow gait execution. Preliminary simulation results demonstrate accurate center-of-mass tracking and stable behavior across gait phases, supporting the feasibility of the proposed method for real-world deployment.