"By letting quadrupeds play soccer, we can push the limits of the artificial intelligence of athletic legged robots," Xiaoyu Huang, Zhongyu Li, Yanzhen Xiang, Yiming Ni, Yufeng Chi, Yunhao Li, Lizhi Yang, Xue Bin Peng, and Koushil Sreenath, the researchers who carried out the study, told TechXplore. "Goalkeeping is an interesting but challenging task that requires the robot to react to the fast-moving ball, sometimes flying in the air, and intercept it using dynamic maneuvers in a very short amount of time (usually within one second). By solving this, we can thus also gain insight about how to create intelligent and dynamic legged robots."

The key objective of the recent work by Huang and his colleagues was to create a four-legged robot goalkeeper that can perfect its skills as it plays, just as a human goalkeeper would. To do this, the researchers developed a reinforcement learning model that trains the robot via a trial-and-error process, rather than through a fixed, human-engineered strategy.

"The robot first learns different locomotion control policies to preform distinct skills, such as sidestep, dive, and jump, while tracking randomized trajectories for the robot's toes," the researchers explained. "Based on these control policies, the robot then learns a high-level planning policy to select an optimal skill and motion to intercept the ball after examining the detected ball position and robot's states."