Robots can learn faster when fed a lot of data

Published June 28, 2014   |   
Derrick Harris

A trio of research projects out of Cornell, MIT and the University of Washington highlight the promise of building robots that can learn to do the things we want them to, but also suggest that patience on behalf of programmers will be a real virtue. Like any application of machine learning, robots will need a whole lot of data and possibly a whole lot of training.

Here is what each project is up to:

1. The University of Washington research involves teaching robots to build things (shapes of out Legos, in this case) based on examples provided by humans. In order to improve accuracy, the researchers also let the robots analyze different, crowdsourced examples of certain objects (e.g., a person or a turtle) and the robots tended to choose those that offered the best balance of simplicity and similarity to the original version.’

2. The MIT research focused on a method for crowdsourcing the learned facts of several robots — or any nodes in a distributed system — in order to achieve a collective intelligence among them. An example of the method in action would be assigning multiple robots to investigate the same building and classify each room based on what’s in it. Each robot might have learned different things about the same room, but they’re able to achieve accurate models by constantly comparing notes until they’ve established ground truth.

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