Watch an AI-Controlled Avatar "Learn" How to Dribble In Real Time - Huge Implications for Games, AR/VR, and Real Life Robotics

What you're looking at above appears to be a pretty big breakthrough in artificial intelligence -- an avatar "learning" how to dribble in real time through AI. DeepDribble is a project from researchers at DeepMotion and Carnegie Mellon, and the idea is to demonstrate how an AI-controlled avatar can acquire the complex and subtle set of integrated physical skills and coordination that humans take for granted:

Basketball at its peak is known for intricate ball handling, quick pivoting, and fake outs. A good player will fluidly transition between have a variety of dribbling skills. In order to recreate this, the trained characters need to be able to transition between separately trained behaviors... Libin Liu and Jessica Hodgins use the same methods for training single skills to create a multi-skill control graph (which allows transition between motion fragments of two different skills). The training is done incrementally, behavior by behavior, to ensure integration occurs with proper transitions for each skill. The result is a player that can perform multiple ball handling skills in a variety of orders, changing course with seamless blending.

"First we build a bio-mechanical model of the digital character, using physics to simulate bones and virtual muscles," Deep Motion's Libin Liu tells me. "We put the digital character in the physically simulated virtual world and let it mimic how real players perform the motions using their muscle forces."

The training process, as you might have imagine, requires quite a lot of power -- but Liu says the learning arc took about as long as a single televised NBA game -- or even half of one:

"Our methods is dynamic, we use linear methods for simple movements and deep reinforcement learning for complex motion skills. Processing power and training time also depend on the length of the reference motion. Overall, it varies from an hour to several hours on a mid-tier server. We have room to further reduce the training time by using parallel processing on the cloud or faster servers."

This greatly differs from how computer-controlled characters are simulated in most videogames, where movement is mapped from the motion captured gestures of real basketball players (in this case). DeepMind COO Xiang Lin says DeepDribble is a completely new approach, and is scalable to VR and AR applications -- and eventually, to real life use cases:

"Our method is fundamentally different from current game 3D animation where recorded motion capture data is replayed in a more static fashion," as she puts it. "Our technique can easily get rid of common artifacts, such as foot-skating and physically unrealistic ball movement, which often appear in even the latest basketball video games.

"Our technology not only can be used in AAA games to create physically realistic animation, but also can be used in VR/AR to generate interactive characters, for example, the kids playing VR game can steal the ball from a virtual professional player. In addition, our technique shares the same foundation with robotics where characters are driven by forces and torques and move under laws of physics. It can potentially be extended to drive real robot to perform hand simulation and human-like motions."

You can read about DeepDribble in more technical detail on the company's blog. This demo of AI movement is among the most impressive and human-like I've seen, suggesting the technology could vastly improve NPC/game character movement -- and, as Xiang Lin suggests, even power numerous real life robotics projects.  (The avatar XPRIZE contest is a likely application.)

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