Just as ImageNet propelled computer vision research, we believe Open X-Embodiment can do the same to advance robotics,” researchers Quan Vuong and Pannag Sanketi noted at the time. “Building a dataset of diverse robot demonstrations is the key step to training a generalist model that can control many different types of robots, follow diverse instructions, perform basic reasoning about complex tasks and generalize effectively.”
At the time of its announcement, Open X-Embodiment contained 500+ skills and 150,000 tasks gathered from 22 robot embodiments. Not quite ImageNet numbers, but it’s a good start. DeepMind then trained its RT-1-X model on the data and used it to train robots in other labs, reporting a 50% success rate compared to the in-house methods the teams had developed.
I’ve probably repeated this dozens of times in these pages, but it truly is an exciting time for robotic learning. I’ve talked to so many teams approaching the problem from different angles with ever-increasing efficacy. The reign of the bespoke robot is far from over, but it certainly feels as though we’re catching glimpses of a world where the general-purpose robot is a distinct possibility.
Simulation will undoubtedly be a big part of the equation, along with AI (including the generative variety). It still feels like some firms have put the horse before the cart here when it comes to building hardware for general tasks, but a few years down the road, who knows?