Researchers have successfully trained an intelligent agent to create basic tools by combining objects.
The work, which uses a new capability to reason about shape, function, and attachment of unrelated parts, is a significant step toward enabling intelligent agents to devise more advanced tools that could prove useful in hazardous—and potentially life-threatening—environments.
The concept may sound familiar. It’s called “MacGyvering,” based on the name of a 1980s—and recently rebooted—television series. In the series, the title character, known for his unconventional problem-solving ability, uses different resources available to him. For years, computer scientists and others have been working to provide robots with similar capabilities.
In this latest work, researchers trained a robot with the new approach, then gave it a set of optional parts and told it to make a specific tool. Much like its human counterparts, the robot first examines the shapes of each part and how one might attach to another.
MacGyvering New Tools
Using machine learning, the robot learns to match form to function—which object shapes facilitate a particular outcome—from numerous examples of everyday objects. For example, by learning that the concavity of bowls enables them to hold liquids, it uses this knowledge when making a spoon. Similarly, researchers taught the robots how to attach objects together from examples of materials that it could pierce or grasp.
In the study, researchers successfully created hammers, spatulas, scoops, squeegees, and screwdrivers.
“The screwdriver was particularly interesting because the robot combined pliers and a coin,” says Lakshmi Nair, a PhD student in the School of Interactive Computing at Georgia Institute of Technology. “It reasoned that the pliers were able to grasp something and says that the coin sort of matched the head of a sc ..
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