Google Set Up Two Robotic Arms For a Game of Infinite Table Tennis

An anonymous reader quotes a report from Popular Science: On the early evening of June 22, 2010, American tennis star John Isner began a grueling Wimbledon match against Frenchman Nicolas Mahut that would become the longest in the sport's history. The marathon battle lasted 11 hours and stretched across three consecutive days. Though Isner ultimately prevailed 70-68 in the fifth set, some in attendance half-jokingly wondered at the time whether the two men might be trapped on that court for eternity. A similarly endless-seeming skirmish of rackets is currently unfolding just an hour's drive south of the All England Club -- at Google DeepMind. Known for pioneering AI models that have outperformed the best human players at chess and Go, DeepMind now has a pair of robotic arms engaged in a kind of infinite game of table tennis. The goal of this ongoing research project, which began in 2022, is for the two robots to continuously learn from each other through competition.

Just as Isner eventually adapted his game to beat Mahut, each robotic arm uses AI models to shift strategies and improve. But unlike the Wimbledon example, there's no final score the robots can reach to end their slugfest. Instead, they continue to compete indefinitely, with the aim of improving at every swing along the way. And while the robotic arms are easily beaten by advanced human players, they've been shown to dominate beginners. Against intermediate players, the robots have roughly 50/50 odds -- placing them, according to researchers, at a level of 'solidly amateur human performance.'

All of this, as two researchers involved noted this week in an IEEE Spectrum blog, is being done in hopes of creating an advanced, general-purpose AI model that could serve as the 'brains' of humanoid robots that may one day interact with people in real-world factories, homes, and beyond. Researchers at DeepMind and elsewhere are hopeful that this learning method, if scaled up, could spark a 'ChatGPT moment' for robotics -- fast-tracking the field from stumbling, awkward hunks of metal to truly useful assistants. 'We are optimistic that continued research in this direction will lead to more capable, adaptable machines that can learn the diverse skills needed to operate effectively and safely in our unstructured world,' DeepMind senior staff engineer Pannag Sanketi and Arizona State University Professor Heni Ben Amor write in IEEE Spectrum.

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