Agile Quadrupedal Locomotion

Agile quadrupedal locomotion research focuses on enabling robots to move quickly and efficiently over challenging terrain while maintaining stability and avoiding collisions. Current efforts concentrate on developing robust control algorithms, often leveraging reinforcement learning and model predictive control, alongside advanced model architectures like hybrid internal models and momentum-aware trajectory optimization to improve state estimation and motion planning. These advancements are significant for enhancing robot capabilities in diverse environments, impacting fields such as search and rescue, logistics, and exploration, where robust and agile locomotion is crucial.