Legged Robot Locomotion

Legged robot locomotion research aims to develop robots capable of robust and efficient movement across diverse terrains, mimicking the agility of animals. Current efforts focus on improving state estimation (using techniques like Kalman filtering and moving horizon estimation), developing adaptable control strategies (employing reinforcement learning, model predictive control, and central pattern generators), and enhancing robot design (incorporating features like extensible feet and multi-robot connection). These advancements are significant for expanding the capabilities of robots in challenging environments, impacting fields like search and rescue, exploration, and industrial automation.