Dribbling a basketball looks deceptively easy. In truth, it takes years of hard work to achieve effortless motion across the court, and, as it turns out, at least as long for developers to model those skills in computer simulations. But a new approach involving artificial intelligence (AI) has the potential to speed things up a bit — at least in the case of developers.

Researchers at Carnegie Mellon University and DeepMotion, a California-based “motion intelligence” startup founded in 2014, have developed a physics-based system that learns dribbling skills from basketball players’ real-life movements.

“This research opens the door to simulating sports with skilled virtual avatars,” Libin Liu, the report’s lead author, told EurekaAlert. “The technology can be applied beyond sport simulation to create more interactive characters for gaming, animation, motion analysis, and in the future, robotics.”

The two teams employed a deep reinforcement learning model — an AI system that mirrors the ways humans respond to environments — in training a basketball-dribbling avatar, setting it loose on a virtual court for trials that numbered in the millions.

It learned in two stages. First, it mastered the art of moving around the court without toppling over or running into obstacles. Then it learned how to control its arms and hands and, by extension, the speed, velocity, and direction of the digital basketball.

Physics-based dribbling is notoriously difficult to reproduce digitally, the researchers noted, because human basketball players make contact with the ball only briefly. Exact details…