Robots learn to perform complex tasks such as setting a table, 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
In their work, the researchers compiled a dataset with information about how eight objects a mug, glass, spoon, fork, knife, dinner plate, small plate, and bowl could be placed on a table in various configurations. Robots learn to perform complex tasks such as setting a table.
A robotic arm first observed randomly selected human demonstrations of setting the table with the objects.
Then, the researchers tasked the arm with automatically setting a table in a specific configuration, in real-world experiments and in simulation, based on what it had seen.
To succeed, the robot had to weigh many possible placement orderings, even when items were purposely removed, stacked, or hidden. Normally, all of that would confuse robots too much.
But the researchers’ robot made no mistakes over several real-world experiments, and only a handful of mistakes over tens of thousands of simulated test runs.
Robots are fine planners in tasks with clear “specifications,” which help describe the task the robot needs to fulfill, considering its actions, environment, and end goal.
Learning to set a table by observing demonstrations, is full of uncertain specifications.
Items must be placed in certain spots, depending on the menu and where guests are seated, and in certain orders, depending on an item’s immediate availability or social conventions.
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. Robots learn to perform complex tasks such as setting a table.
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.
In simulations asking the robot to set the table in different configurations, it only made six mistakes out of 20,000 tries.
In real-world demonstrations, it showed behavior similar to how a human would perform the task. If an item wasn’t initially visible, for instance, the robot would finish setting the rest of the table without the item. Robots learn to perform complex tasks such as setting a table.
Next, the researchers hope to modify the system to help robots change their behavior based on verbal instructions, corrections, or a user’s assessment of the robot’s performance.
Researcher said, We want to develop methods for the system to naturally adapt to handle those verbal commands, without needing additional demonstrations.