16 hours ago by Aaron Dubrow Researchers developed a new approach that allows a robot to plan its activity to accomplish an assigned task. Credit: Siddharth Srivastava, Shlomo Zilberstein, Abhishek Gupta, Pieter Abbeel, Stuart Russell <\/p>\n
People typically consider doing the laundry to be a boring chore. But laundry is far from boring for artificial intelligence (AI) researchers like Siddharth Srivastava, a scientist at the United Technologies Research Center, Berkeley. <\/p>\n
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. People can easily cope with this type of uncertainty and come up with a simple plan. But roboticists for decades have struggled to design an autonomous system able to do what we do so casuallyclean our clothes. <\/p>\n
In work done at the University of California, Berkeley, and presented at the Association for Advancement of Artificial Intelligence conference in Austin, Srivastava (working with Abhishek Gupta, Pieter Abbeel and Stuart Russell from UC Berkeley and Shlomo Zilberstein from University of Massachusetts, Amherst) demonstrated a robot that is capable of doing laundry without any specific knowledge of what it has to wash. <\/p>\n
Earlier work by Abbeel's group had demonstrated solutions for the sorting and folding of clothes. The laundry task serves as an example for a wide-range of daily tasks that we do without thinking but that have, until now, proved difficult for automated tools assisting humans. <\/p>\n
\"The widely imagined helper robots of the future are expected to 'clear the table,' 'do laundry' or perform day-to-day tasks with ease,\" Srivastava said. \"Currently however, computing the required behavior for such tasks is a challenging problemparticularly when there's uncertainty in resource or object quantities.\" <\/p>\n
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. <\/p>\n
\"The main issue is how to develop what we call 'generalized plans,'\" said Zilberstein, a professor of computer science and director of the Resource Bound Reasoning Lab at UMass Amherst. \"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.\" <\/p>\n
The researchers' key insight was to use human behaviorthe almost unconscious action of pulling, stuffing, folding and pilingas a template, adapting both the repetitive and thoughtful aspects of human problem-solving to handle uncertainty in their computed solutions. <\/p>\n
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. <\/p>\n
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