Researchers from
Brown University are developing a new algorithm to help robots better plan
their actions in complex environments. It's designed to help robots be more
useful in the real world, but it's being developed with the help of a virtual
world, that of the video game Minecraft. Basic action planning, while easy for
humans, is a frontier of robotics. Part of the problem is that robots don't
intuitively ignore objects and actions that are irrelevant to the task at hand.
For example, if someone asked you to empty the trashcan in the kitchen, you
would know there's no need to turn on the oven or open the refrigerator. You'd
go right to the trashcan. Robots, however, lack that intuition. Most approaches
to planning consider the entire set of possible objects and actions before
deciding which course to pursue. In other words, a robot might actually
consider turning on the oven as part of its planning process for taking out the
trash. In complex environments, this leads to what computer scientists refer to
as the ‘state-space explosion’ (an array of choices so large that it boggles
the robot mind). The algorithm augments standard robot planning algorithms
using ‘goal-based action priors’, sets of objects and actions in a given space
that are most likely to help an agent achieve a given goal. The priors for a given task can be supplied
by an expert operator, but they can also be learned by the algorithm itself
through trial and error.
The game
Minecraft, as it turns out, provided an ideal world to test how well the
algorithm learned action priors and implemented them in the planning process.
For the uninitiated, Minecraft is an open-ended game, where players gather
resources and build all manner of structures by destroying or stacking 3D
blocks in a virtual world. At over 100 million registered users, it's among the
most popular video games of all time. After the algorithm ran through a number
of trials of a given task to learn the appropriate priors, the researchers
moved to a new domain that it had never seen before to see if it could apply
what it learned. Indeed, the researchers showed that, armed with priors, their
Minecraft agents could solve problems in unfamiliar domains much faster than
agents powered by standard planning algorithms. Having honed the algorithm in
virtual worlds, the researchers then tried it out in a real robot. They used
the algorithm to have a robot help a person in the task of baking brownies. The
algorithm was supplied with several action priors for the task. For example,
one action prior let the robot know that eggs often need to be beaten with a
whisk. So when a carton of eggs appears in the robot's workspace, it is able to
anticipate the cook's need for a whisk and hand him one. The work also shows
the potential of virtual spaces like Minecraft in developing solutions for
real-world robots and other artificial agents.
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