Quadruped Locomotion

Quadruped locomotion research aims to develop robust and versatile control algorithms enabling robots to navigate diverse terrains. Current efforts focus on learning-based approaches, employing techniques like reinforcement learning (often with decision transformers or central pattern generators), model predictive control, and transformer-based architectures for sensor integration and efficient gait generation. These advancements are crucial for creating more adaptable robots for applications ranging from search and rescue to industrial automation, improving both locomotion efficiency and robustness in unstructured environments.