Leap Motion’s AR table tennis is a long way from your parents’ Pong
Leap Motion has a brand-new game for early testers of its Project Northstar augmented reality headset to play around with: Table tennis. The game leverages not only the headset itself but a new paddle controller as well, and comes with its own A.I. opponent so all you really need is a table. In the demonstration we see the headset wearer bouncing a virtual ball between paddles and having a full game with a floating digital paddle on the opposite end of the table. Better yet, they never have to go and pick up the ball when it falls off the table.
Augmented reality might not have received quite the same push as virtual reality just yet — some think we’re still a few years away from its mainstream adoption — but there are plenty of companies working away at it. Microsoft’s HoloLens is one prominent example, but Leap Motion’s Project Northstar turned heads earlier this year for its expansive headset design and development still continues apace.
As much as the table tennis demonstration looks like a fun way to spend some time in augmented reality, Leap Motion software engineer Johnathon Selstad said that he sees this as more of an example of mixed reality skills training. He sees augmented reality’s greatest potential in helping people to learn things in a partly digital space. Skills they can then apply to the real world. In the case of table tennis, simply practicing a game with real-world motions with an A.I. opponent could help to improve your game without needing to play with people in the real world.
“In VR, we can shape the experience to optimize learning a task or behavior,” Selstad explained. “AR elevates this potential with familiar real-world environments, allowing us to contextualize learned skills. By overlaying virtual indicators and heuristics onto the user’s view, we can even help them develop a deeper intuition of the system.”
In the context of a table tennis game, physics projections of where the ball will go can be shown to the player to help them learn to intuitively understand how it will behave. While such a lesson may not translate 100 percent to the real world, it’s certainly more useful than playing a similar game with a controller or with no real-world environment around you.
That’s especially true when it comes to A.I. opponents. As we saw with humans vs. A.I. matchups in games like Go, when near-perfect A.I. play against top-tier human players, the humans typically lose but improve themselves by learning from their artificial opponents. AR has the potential to make such gameplay more applicable to the real world.
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