Now, roboticists have made major breakthroughs in that pursuit. One was figuring out how to combine different sorts of data and then make it all useful and legible to a robot. Take washing dishes as an example. You can collect data from someone washing dishes while wearing sensors. Then you can combine that with teleoperation data from a human doing the same task with robotic arms. On top of all that, you can also scrape the internet for images and videos of people doing dishes.
By merging these data sources properly into a new AI model, it’s possible to train a robot that, though not perfect, has a massive head start over those trained with more manual methods. Seeing so many ways that a single task can be done makes it easier for AI models to improvise, and to surmise what a robot’s next move should be in the real world.
It’s a breakthrough that’s set to redefine how robots learn. Robots that work in commercial spaces like warehouses are already using such advanced training methods, and the lessons we learn from those experiments could lay the groundwork for smart robots that help out at home.
#Fastlearning #robots #Breakthrough #Technologies
