Perceptive Locomotion

Perceptive locomotion focuses on enabling robots, particularly legged robots, to move effectively and safely in unstructured environments by integrating real-time perception with locomotion control. Current research emphasizes end-to-end model-based learning approaches, often employing neural networks like proximal alternating-minimization networks or recurrent encoders, and leveraging techniques such as control barrier functions and model predictive control to ensure safe and robust motion. This field is crucial for advancing autonomous robotics, with applications ranging from search and rescue to industrial automation, by enabling robots to navigate and interact with complex, unpredictable environments.