Multi Contact Locomotion

Multi-contact locomotion focuses on enabling robots to move efficiently and robustly using multiple points of contact with the environment, mimicking the agility of animals. Current research emphasizes developing advanced control algorithms, such as model predictive control and those incorporating central pattern generators, often coupled with sophisticated state estimation techniques like Kalman filters, to manage complex interactions between the robot and its surroundings. These advancements are driven by the need for improved robustness and adaptability in challenging terrains and tasks, with applications ranging from search and rescue robots to advanced manufacturing. The development of reduced-order models and hybrid approaches that combine learning-based and classical methods are also key areas of focus.