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Fast learner: El-E is able to retrieve objects that a human indicates with a handheld laser. Here, El-E hands its creator Charles Kemp a cloth towel, traditionally a difficult object for robots to pick up because of its malleability. Using visual and tactile sensors on its arm and hand, the robot can grasp items that it has not encountered before.

The hand gripper descends and orients in the best way to grip the object, while sensors in the fingertips prevent the hand from either crushing the object or letting it slip out of its grasp. If El-E can’t grab the object the first time it tries, its feedback system kicks in to reevaluate the object’s position with the laser range finder and try other orientations with the gripper. “Having the robot know that it’s failed and trying different strategies improves the performance significantly,” says Kemp. While robots can do complex tasks in rigidly controlled environments like a car factory, unpredictable situations and objects are, right now, a robot’s bane. In the past, researchers have had robots memorize the shape of objects. Currently, a host of researchers are teaching robots to identify new objects in different ways. Ng’s Stair robot, for example, relies on machine-learning techniques, through which it is shown how to pick up types of objects until it eventually devises its own strategies. (See “Your Robotic Personal Assistant.”)

So far, El-E can pick up cups, bottles, phones, and dish towels (tricky for robots because of their shapelessness). When El-E has grasped an object, it utters a whimsical “Bob’s your uncle” and follows the laser back to the user or to a designated surface. Using standard face-detection software, El-E proclaims “Life-form detected” as it offers the object.

El-E is a “very compelling demonstration of what is possible today,” says Josh Smith, a senior research scientist at Intel Research Seattle, who works on robotic gripping. He adds that El-E is just the beginning. Researchers still have to figure out how robots can grasp more complexly shaped or heavier objects, as well as objects in cluttered environments or stored among many identical items (forks in a drawer, for example). The continued development of robot fetching is essential, Smith says, because it is a “building block from which you can build many other personal robot applications” and potentially even military ones.

“I think that someday in the future, home robots will be as common in our houses as cars are in our garage today, and as indispensible,” says Ng, who is in the process of teaching his Stair robot to microwave a frozen burrito. While the hardware and machinery for making fully functional robot assistants exist, the software needs to be improved before people have robots tidying up their houses, Ng says.

Aside from making everyday life easier for people, robot helpers would be able to let the elderly and those with motor impairments live more independently. For example, El-E could retrieve a fallen prescription bottle or phone–something that might be impossible for a person with severe disabilities. The next step for El-E is to work with people with ALS, a devastating neurodegenerative disorder that severely impairs mobility. “There’s an enormous opportunity to improve people’s quality of life and make an impact on health care,” says Kemp. Ongoing studies with the Emory ALS center this summer will test El-E with ALS patients to see if it can successfully meet their needs. Eventually, the team hopes to have El-E flick light switches and open doors.

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Credits: Rob Felt/Copyright Georgia Institute of Technology 2008

Tagged: Computing, robotics, robots, lasers, robotic arm, robotic assistant

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