A combined team of roboticists from Stanford University and the Toyota Research Institute has found that adding audio data to visual data when training robots helps to improve their learning skills. The team has posted their research on the arXiv preprint server.
The researchers noted that virtually all training done with AI-based robots involves exposing them to a large amount of visual information, while ignoring associated audio. They wondered if adding microphones to robots and allowing them to collect data regarding how something is supposed to sound as it is being done might help them learn a task better.
For example, if a robot is supposed to learn how to open a box of cereal and fill a bowl with it, it may be helpful to hear the sounds of a box being opened and the dryness of the cereal as it cascades down into a bowl. To find out, the team designed and carried out four robot-learning experiments.
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