Robotics
| MIT researchers have developed software for robots that enables them to be more “aware” of their own limitations, such as knowing the whereabouts of an object, or its own location within a room. |
Most successful robots today tend to be used either in fixed, carefully controlled environments, such as manufacturing plants, or for performing fairly simple tasks such as vacuuming a room, says Leslie Pack Kaelbling, the Panasonic Professor of Computer Science and Engineering at MIT.
Carrying out complicated sequences of actions in a cluttered, dynamic environment such as a home will require robots to be more aware of what they do not know, and therefore need to find out, Kaelbling says. That is because a robot cannot simply look around the kitchen and determine where all the containers are stored, for example, or what you would prefer to eat for dinner. To find these things out, it needs to open the cupboards and look inside, or ask a question.
“I would like to make a robot that could go into your kitchen for the first time, having been in other kitchens before but not yours, and put the groceries away,” Kaelbling says.
Kaelbling's paper was recently accepted for publication in the International Journal of Robotics Research, she and CSAIL colleague Tomas Lozano-Perez describe a system designed to do just that, by constantly calculating the robot’s level of uncertainty about a given task, such as the whereabouts of an object, or its own location within the room.
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| Carnegie Melon's robot HERB is not fully aware of its environment |
So, for example, if the robot were trying to pick up a box of cereal from a shelf, it might decide its uncertainty about the position of the object was too high to attempt grasping it. Instead, it would first take a closer look at the object, in order to get a better idea of its exact location, Kaelbling says. “It’s thinking always about its own belief about the world, and how to change its belief, by taking actions that will either gather more information or change the state of the world.”
The system also simplifies the process of developing a strategy for performing a given task by making up its plan in stages as it goes along, using what the team calls hierarchical planning in the now.
“There is this idea in AI that we’re very worried about having an optimal plan, so we’re going to compute very hard for a long time, to ensure we have a complete strategy formulated before we begin execution,” Kaelbling says.
But in many cases, particularly if the environment is new to the robot, it cannot know enough about the area to make such a detailed plan in advance, she says.
So instead the system makes a plan for the first stage of its task and begins executing this before it has come up with a strategy for the rest of the exercise. That means that instead of one big complicated strategy, which consumes a considerable amount of computing power and time, the robot can make many smaller plans as it goes along.
The drawback to this process is that it can lead the robot into making silly mistakes, such as picking up a plate and moving it over to the table without realizing that it first needs to clear some room to put it down, Kaelbling says.
But such small mistakes may be a price worth paying for more capable robots, she says: “As we try to get robots to do bigger and more complicated things in more variable environments, we will have to settle for some amount of suboptimality.”
In addition to household robots, the system could also be used to build more flexible industrial devices, or in disaster relief, Kaelbling says.
SOURCE MIT Top Image: Allegra Boverman and Christine Daniloff/MIT
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