Complex Terrain
Complex terrain navigation presents a significant challenge for robots, demanding robust locomotion and perception systems capable of handling diverse and unpredictable surfaces. Current research focuses on developing advanced control algorithms, often leveraging reinforcement learning and neural networks (including transformers) to enable robots—ranging from wheeled and legged platforms to UAVs—to traverse challenging environments, including steep slopes, uneven surfaces, and obstacles. These advancements are crucial for expanding the capabilities of robots in search and rescue, exploration, and other applications requiring autonomous operation in unstructured environments, improving efficiency and reliability in these domains.
Papers
Autonomous Excavation of Challenging Terrain using Oscillatory Primitives and Adaptive Impedance Control
Noah Franceschini, Pranay Thangeda, Melkior Ornik, Kris Hauser
Traverse the Non-Traversable: Estimating Traversability for Wheeled Mobility on Vertically Challenging Terrain
Chenhui Pan, Aniket Datar, Anuj Pokhrel, Matthew Choulas, Mohammad Nazeri, Xuesu Xiao
Verti-Selector: Automatic Curriculum Learning for Wheeled Mobility on Vertically Challenging Terrain
Tong Xu, Chenhui Pan, Xuesu Xiao