Hae-won Park - Hardware Design and Control Algorithms for Agile and Versatile Legged Robots

Biological animals excel at navigating complex and challenging environments, leveraging their hardware to perform dynamic motions and athletic skills that overcome diverse obstacles. Despite recent advancements, robotic systems still lack comparable dynamic locomotion capabilities. In this talk, I will present our lab's efforts to bridge this gap by developing robust hardware and effective control algorithms that enable both agility and robustness in legged robots. I will begin by introducing our quadruped robot platforms: HOUND, designed for high-speed locomotion on complex terrains, and MARVEL, designed for agile and versatile climbing. HOUND incorporates custom electric actuators, while MARVEL uses magnetic feet to generate climbing force. I will then discuss the control algorithms that drive these robots, leveraging model predictive control and reinforcement learning techniques. Finally, I will present our latest learning-based locomotion control framework, capable of synthesizing and executing diverse dynamic motions across various terrains. This framework combines a low-level skill policy, pre-trained using a large offline dataset generated via trajectory optimization, with a reinforcement learning policy trained on diverse terrains. With this integrated approach, HOUND achieves speeds of up to 9.5 m/s, making it the fastest legged robot, while MARVEL can traverse ceilings and vertical walls at speeds of up to 0.5 m/s and 0.7 m/s, respectively.