By observing humans, robots learn to perform complex tasks, such as setting a table by Rob Matheson, Writer (computer science and technology).
Training interactive robots may one day be an easy job for everyone,
even those without programming expertise. Roboticists are developing
automated robots that can learn new tasks solely by observing humans. At
home, you might someday show a domestic robot how to do routine chores.
In the workplace, you could train robots like new employees,
showing
them how to perform many duties.
Making progress on that vision, MIT researchers have designed a
system that lets these types of robots learn complicated tasks that
would otherwise stymie them with too many confusing rules. One such task
is setting a dinner table under certain conditions.
At its core, the researchers’ “Planning with Uncertain
Specifications” (PUnS) system gives robots the humanlike planning
ability to simultaneously weigh many ambiguous — and potentially
contradictory — requirements to reach an end goal. In doing so, the
system always chooses the most likely action to take, based on a
“belief” about some probable specifications for the task it is supposed
to perform...
Following criteria
The researchers also developed several criteria that guide the robot toward satisfying the entire belief over those candidate formulas. One, for instance, satisfies the most likely formula, which discards everything else apart from the template with the highest probability. Others satisfy the largest number of unique formulas, without considering their overall probability, or they satisfy several formulas that represent highest total probability. Another simply minimizes error, so the system ignores formulas with high probability of failure.
Designers can choose any one of the four criteria to preset before training and testing. Each has its own tradeoff between flexibility and risk aversion. The choice of criteria depends entirely on the task. In safety critical situations, for instance, a designer may choose to limit possibility of failure. But where consequences of failure are not as severe, designers can choose to give robots greater flexibility to try different approaches.
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Source: The MIT Tech