GoKawiilWatching Eka’s robot in action reminds me of the first time I tried talking to ChatGPT. The robots are so fluid, so natural-seeming, that I can’t help but feel there’s something genuinely intelligent, if not quite human, behind them.
In a conference room not far from the robots, Eka’s cofounders, Pulkit Agrawal, a professor at MIT, and Tuomas Haarnoja, an ex-Google DeepMind robotics researcher, lay out their vision for the curious new machine. “A couple of years ago, we realized that dexterity can finally be cracked,” Agrawal says. Eka’s robot demos suggest that the company’s approach should enable real robot dexterity with further training. If that’s true, it could revolutionize how robots are used—not only in factories and warehouses but also in shops, restaurants, even households. “Trillions of dollars flow through the human hand,” Agrawal says. “To me, this is the biggest problem in the world to be solved.”
The two men believe they are halfway there. Solving dexterity, they say, is now just a question of scaling up the approach.
The fastest humans can solve a Rubik’s Cube in about three seconds. In those same three seconds, a computer with a virtual Rubik’s Cube could solve thousands of variations of the puzzle. As the Austrian computer scientist Hans Moravec famously noted in the late 1980s, the tasks that often seem hardest to us humans are child’s play for a machine; the things a child does without thinking are often a struggle for machines. Moravec suggested that the ability to interact with the physical realm evolved so long ago that for us it’s innate, more so than “higher-level” reasoning. The question has been: Can we impart that embodied intelligence to machines?
One of Eka's newer machines with a three-point hand. Photograph: Tony Luong
Back in October 2018, about four years before launching ChatGPT, OpenAI created Dactyl, a robotic hand that later used AI to solve a Rubik’s Cube. The company took an off-the-shelf hand from Shadow Robot and created a detailed simulation of its joints, servos, motors, and more—a virtual hand holding a virtual cube. Using reinforcement learning, which combines experimentation with positive and negative feedback, OpenAI trained an artificial neural network to manipulate the digital cube over and over. After many thousands of repetitions of wiggling its virtual fingers, Dactyl had figured out how to move the facets of the real thing.