Learning Perceptive Legged Robot Locomotion in the Real World A Systematic Review

Abstract

Perception is essential for legged locomotion as it enables robots to anticipate upcoming terrains and obstacles, facilitating adaptive traversal of challenging environments. Recent advancements in learning methodologies and legged locomotion have fostered research on the integration of perception into legged robot locomotion controllers, allowing them to operate effectively in a variety of real-world environments that are inaccessible to wheeled or tracked robots. This review aims to summarize recent developments in perceptive legged robot locomotion and identify potential future research directions. Specifically, we restrict our scope to studies employing learning-based techniques. We examine two primary topics 1) the applications and capabilities afforded by perceptive locomotion and 2) the methodologies for integrating perception into locomotion systems. We survey the characteristics, progress, and challenges associated with each application and methodology as well as the decisions involved. Finally, we provide a comprehensive overview of the current challenges and potential future research perspectives in perceptive legged robot locomotion.