In years to come, robots could assist human users in a variety of ways, both when they are inside their homes and in other settings. To be more intuitive, robots should be able to follow natural language commands and instructions, as this allows users to communicate with them just as they would with other humans.

With this in mind, researchers at MIT's Center for Brains, Minds & Machines have recently developed a sampling-based robotic planner that can be trained to understand sequences of natural language commands. The system they developed, presented in a paper pre-published on arXiv, combines a deep neural network with a sampling-based planner.

"It's quite important to ensure that future robots in our homes understand us, both for safety reasons and because language is the most convenient interface to ask for what you want," Andrei Barbu, one of the researchers who conducted the study, told TechXplore. "Our work combines three lines of research: robotic planning, deep networks, and our own work on how machines can understand language. The overall objective is to give a robot only a few examples of what a sentence means and have it follow new commands and new sentences that it never heard before."

The far-reaching goal of the research carried out by Barbu and his colleagues is to better understand body language communication. In fact, while the functions and mechanisms behind spoken communication are now well understood, most communication that takes place among animals and humans is non-verbal.

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