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Now roboticists have made great strides in that search. One was figuring out how to combine different types of data and make it useful and readable to a robot. Let’s take washing dishes as an example. You can collect data from someone washing dishes while wearing sensors. You can then combine this with teleoperation data from a human doing the same task with robotic arms. On top of all that, you can also search the internet for pictures and videos of people washing dishes.
By properly combining these data sources into a new model of artificial intelligence, it is possible to train a robot that, although not perfect, has a great advantage over those trained by manual methods. Seeing so many ways a single task can be done makes it easy for AI models to improvise and guess what the robot’s next move should be in the real world.
It’s a discovery that will redefine the way robots learn. Robots working in commercial spaces like warehouses already use such advanced training methods, and the lessons we learn from these experiments could lay the groundwork for smart robots that help in the home.
