Now, roboticists have made major breakthroughs in that pursuit. One was determining how you can mix different forms of data after which make all of it useful and legible to a robot. Take washing dishes for instance. You’ll be able to collect data from someone washing dishes while wearing sensors. Then you definately can mix that with teleoperation data from a human doing the identical task with robotic arms. On top of all that, you can too scrape the web for images and videos of individuals doing dishes.
By merging these data sources properly right into a recent AI model, it’s possible to coach a robot that, though not perfect, has an enormous head start over those trained with more manual methods. Seeing so some ways that a single task will be done makes it easier for AI models to improvise, and to surmise what a robot’s next move must be in the true world.Â
It’s a breakthrough that’s set to redefine how robots learn. Robots that work in industrial spaces like warehouses are already using such advanced training methods, and the teachings we learn from those experiments could lay the groundwork for smart robots that help out at home.Â
