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Computer animation has never been better, but there’s still plenty of room for improvement. If we’re ever going to venture through to other side of the uncanny valley—that place where viewers can’t discern between what’s simulated and what’s real—it’ll be because we’ve finally imbued our virtual characters with natural appearances and movement. To that end, UC Berkeley graduate student Xue Bin “Jason” Peng, along with his colleagues, have combined two techniques—motion-capture technology and deep-reinforcement computer learning—to create something completely new: a system that teaches simulated humanoids how to perform complex physical tasks in a highly realistic manner. Learning from scratch, and with limited human intervention, the digital characters learned how to kick, jump, and flip their way to success. What’s more, they even learned how to interact with objects in their environment, such as barriers placed in their way or objects hurled directly at them. Normally, computer animators have to manually create custom controllers for every skill or task. These controllers are fairly granular, and include discrete skills such as walking, running, tumbling, or whatever the character needs to do. The motions created with this technique look decent, but each of them have to crafted individually by hand. Another approach is to exclusively use reinforcement learning methods, such as DeepMind’s GAIL. This technique is impressive, as simulated humanoids learn how to do things from scratch—but it often produces bizarre, unpredictable, and highly unnatural results. The new system, dubbed DeepMimic, works a bit differently. Instead of pushing the simulated character towards a specific end goal, such as walking, DeepMimic uses motion-capture clips to “show” the AI what the end goal is supposed to look like. In experiments, Bin’s team took motion-capture data from more than 25 different physical skills, from running and throwing to jumping and backflips, to “define the desired style and appearance” of the skill, as Peng explained at the Berkeley Artificial Intelligence Research (BAIR) blog. The virtual characters tripped, stumbled, and fell flat on their faces repeatedly until they finally got the movements right. It took about a month of simulated “practice” for each skill to develop, as the humanoids went through literally millions of trials trying to nail the perfect backflip or flying leg kick. But with each failure came an adjustment that took it closer to the desired goal. Using this technique, the researchers were able to produce agents who behaved in a highly realistic, natural manner.

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