Robust Locomotion

Robust locomotion research aims to enable robots to move reliably and efficiently across diverse and unpredictable terrains, mirroring the adaptability of animals. Current efforts focus on developing controllers using reinforcement learning, model predictive control, and hybrid approaches that integrate proprioceptive and exteroceptive sensing, often incorporating advanced architectures like neural networks and vision-language models. These advancements are crucial for expanding the capabilities of robots in challenging environments, impacting fields such as search and rescue, logistics, and exploration.