Google Deepmind takes the next step toward general-purpose robots


With a new dataset, Deepmind has partnered with many other institutions to fill the data gap in robot training and enable generalization of robot capabilities across robot types. Early results are promising.

Google Deepmind, in collaboration with 33 academic labs, has released a new dataset and models aimed at promoting generalized learning in robotics across different types of robots.

The data comes from 22 different types of robots. The goal is to develop robot models that can better generalize their capabilities across different types of robots.

Toward a general-purpose robot

Until now, a separate robot model had to be trained for each task, each robot, and each environment. This places high demands on data collection. In addition, the slightest change in a variable meant that the process had to start all over again, according to Deepmind.



The goal of the Open-X initiative is to find a way to pool knowledge about different robots (embodiments) and train a universal robot. This idea led to the development of the Open-X embodiment dataset and RT-1-X, a robot transformer model derived from RT-1 (Robotic Transformer-1) and trained on the new dataset.

Tests in five different research labs showed an average 50 percent increase in task completion success when RT-1-X took control of five common robots compared to the robot-specific control models.

Image: Deepmind

A dataset for general-purpose robot training

The Open X Embodiment dataset was developed in collaboration with academic research labs from more than 20 institutions. It aggregates data from 22 robots representing more than 500 capabilities and 150,000 tasks in more than one million workflows.

The dataset is an important tool for training a generalist model that can control many types of robots, interpret different instructions, make basic inferences about complex tasks, and generalize efficiently, according to Deepmind.



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