This was the technique that a team from the University of Washington proposed at a recent conference. They described their technique as a goal-based imitation learning framework that utilises crowd-sourcing as a major source of human demonstration data. They demonstrated their approach by making a robot build models with basic building blocks using knowledge gained from people around it as well as online crowd sources.

They also showed how such a technique could support human-robot collaboration tasks such as goal inference through object-part classification and missing-part prediction.

It is obvious that machine learning is making huge strides. Demonstrations of autonomous cars, intelligent industrial systems, smart drones, and even context-aware apps, are all signs of advancement of artificial intelligence—all of which is reflected in robotics too, albeit at much higher levels. There is no end to the examples we can give. Diego-San, a boy-like robot developed at the University of California, San Diego; user-friendly machine intelligence tools like PredictionIO that help develop self-learning robots and devices; Pepper, the chatty Japanese android that can understand people’s emotions and around 70 to 80 per cent of their spontaneous conversation. The list will go on and on, till we arrive at the day when robots pass the Turing test with ease!


The author is a technically-qualified freelance writer, editor and hands-on mom based in Chennai

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