Blind Locomotion

Blind locomotion research focuses on enabling robots to navigate diverse and challenging terrains without relying on visual input, primarily using proprioceptive sensors and learned control policies. Current efforts concentrate on developing robust and adaptable locomotion controllers, often employing reinforcement learning techniques, transformer models, and privileged learning frameworks, sometimes augmented with model predictive control, to achieve agile and stable movement across various surfaces and obstacles. This research is significant for advancing robotics capabilities in unstructured environments and holds potential for applications such as search and rescue, assistive technologies for the visually impaired, and expanding the operational domains of legged robots.