Optimal Torque Distribution via Dynamic Adaptation for Quadrupedal Locomotion on Slippery Terrains

As legged robots continue to evolve, new control methods are being developed to provide fast, robust, accurate and computationally efficient algorithms for traversing challenging environments. This paper presents a realtime adaptive locomotion controller for quadrupeds, designed to maintain stability and controllability on various surfaces, including highly slippery terrains. The proposed approach optimizes control effort distribution based on the probability of slippage by utilizing a surface-independent adaptation layer. By balancing the robot's redundant kinematic system through rank relaxation-similar to loosening constraints in optimization problems-this method demonstrates significant performance improvements. Unlike Reinforcement Learning (RL) approaches, which depend on pre-trained policies and may struggle to adapt velocity tracking control across different terrains, our method rapidly adjusts to changing conditions, as validated by extensive simulation experiments.