Now Household Robots That Can Do Your Laundry
A team of artificial intelligence (AI) researchers has demonstrated a robot that is capable of doing laundry without any specific knowledge of what it...
Washington: A team of artificial intelligence (AI) researchers has demonstrated a robot that is capable of doing laundry without any specific knowledge of what it has to wash.
To AI experts, programming a robot to do the laundry represents a challenging planning problem because current sensing and manipulation technology is not good enough to identify precisely the number of clothing pieces that are in a pile and the number that are picked up with each grasp.
Siddharth Srivastava of the United Technologies Research Center, Berkeley said that the widely imagined helper robots of the future are expected to "clear the table," "do laundry" or perform day-to-day tasks with ease, however, computing the required behavior for such tasks is a challenging problem, particularly when there's uncertainty in resource or object quantities.
Humans, on the other hand, solve such problems with barely a conscious effort. In their work, the researchers showed how to compute correct solutions to problems by using some assumptions about the uncertainty.
Shlomo Zilberstein from University of Massachusetts, Amherst added that the main issue is how to develop what is call "generalized plans," and these are plans that don't just work in a particular situation that is very well defined and gets you to a particular goal that is also well defined, but rather ones that work on a whole range of situations and you may not even know certain things about it.
The researchers' key insight was to use human behavior, the almost unconscious action of pulling, stuffing, folding and piling, as a template, adapting both the repetitive and thoughtful aspects of human problem-solving to handle uncertainty in their computed solutions.
By doing so, they enabled a PR2 robot to do the laundry without knowing how many and what type of clothes needed to be washed.
The framework that researchers developed combines several popular planning paradigms that have been developed in the past using complex control structures such as loops and branches and optimizes them to run efficiently on modern hardware. It also incorporates an effective approach for computing plans by learning from examples, rather than through rigid instructions or programs.