Technology | DOI: 10.1145/1941487.1941494
Gregory Goth
I, Domestic Robot
With recent advances in laser rangefinders, faster algorithms,
and open source robotic operating systems, researchers are increasing
domestic robots’ semantic and situational awareness.

InDUstrIaL roBots, fIXeD-Lo- CatIon and single-function machines, have long been sta- ples of advanced manufactur- ing settings. Medical robots, which can help surgeons operate with smaller incisions and cause less blood loss than traditional surgical methods, are making fast inroads in metropolitan and suburban hospitals. Rescue robots, included wheeled and snake-like robots, are increasingly common, and were deployed in the search for survivors in the aftermath of the earthquake and tsunami that recently struck Japan. On the other hand, the promise of multipurpose domestic assistance robots, capable of a wide range of tasks, has been a distant goal.

However, recent advances in hardware such as laser rangefinders, open source robotic operating systems, and faster algorithms have emboldened researchers. Robots are now capable of folding laundry, discerning where to place an object on cluttered surfaces, and detecting the presence of people in a typical room setting.

“It’s easy for me to be optimistic, but if robots aren’t actually being useful and fairly widespread in 10 years, then I will be fairly disappointed,” says Charles Kemp, assistant professor of biomedical engineering at Georgia Tech University.

sensors enable awareness

In recent months, numerous research teams have published papers detailing advances in robots’ perceptual capabilities. These perceptual advances enable the robots’ mechanical components to complete domestic tasks hitherto impossible.

Kemp and his research team have pioneered semantic and situational awareness in robots through several methods, including the creation of radio frequency identification (RFID)

Willow Garage’s PR2, an open source robotics research and development platform.

 

semantic tags on common objects such as light switches, and by combining sensor data taken from both two-dimensional camera data and three-dimensional point clouds gathered by laser rangefinders.

University of Bonn researchers Jörg Stückler and Sven Behnke also demonstrated success, using a combination of 2D laser and camera sensors. They programmed a mobile service robot to combine laser rangefinder data that hypothesizes the presence of a person’s legs and torso with 2D frontal and profile images of the detected face.

Stückler and Behnke also modeled the semantic probability of detecting a person’s presence in different locations of a room—high probability in a chair and low probability on a bookshelf, for instance— and supplied the robot with that knowledge. The prior knowledge of the room semantics and precalculat-

ed range of likely valid facial height helps the Bonn researchers discern false positive returns.

Steve Cousins, CEO of Willow Garage, which manufactures the open platform general-purpose PR2 robot, says further advances in perceptual capabilities may be even more likely with the recent debut of sensing technology that enables a computer to analyze an area in three dimensions and then to create what the technology’s manufacturer, PrimeSense, calls a synchronized depth image. The technology sells for less than 1/20th of the de facto standard research rangefinder, which costs about $5,000. Both Cousins and Kemp believe the low cost of the PrimeSense sensor (it is a key component of Microsoft’s Kinect gaming system) may lead to a surge in situational and semantic robotic research. Kemp says his team recently installed one of the new sensors to its PR2.

In essence, Kemp says its real-time technology greatly simplifies a robot’s data-gathering process.

Prior to installing the new sensor, on projects such as the work on making the robot discern clutter, he says “we had to tilt the laser rangefinder up and down, then snap a picture and relate those two things. That’s a pretty slow process and really expensive.”

 

a semantic Database Kemp says there are two distinct research areas for similar problem sets in domestic robotics: those related to perceptual problem sets, and those related to mechanical awareness. For example, a roving robot meant to help a person with basic housekeeping chores must not only know how to differentiate a refrigerator door handle from a light switch, but it must also be able to calculate which approach its arms must take, and how firmly it must grip the respective levers.