In the experiment using RFID tags, Kemp created a semantic database the robot could refer to after identifying an object. The database contains instructions on how the robot should act upon an object. For example, under “actions,” after a robot identifies and contacts a light switch, the commands are “off: push bottom” and “on: push top.” Each of these actions is further sub-programmed with a force threshold the robot should not exceed.

Kemp is also investigating another approach to providing robots with such situational awareness that entails equipping human subjects with touch sensors. The sensors are held during the completion of common tasks such as opening refrigerators and cabinet doors in multiple settings. The information on the kinematics and forces of such actions is then entered into a database a service robot can access when it approaches one of these objects en route to performing a task.

“If the robot knows it is a refrigera-
tor, it doesn’t have to have worked with
that specific refrigerator before,” he
says. “If the semantic class is ‘refrigera-
tor’ it can know what to expect and be
more intelligent about its manipula-
tion. This can make it more robust and
introduces this notion of physically
grounded common sense about things
like how hard you should pull when
opening a door.”
Offboard computation akin to the
kinematic database is also being done
to improve already successful robotic
tasks. A team of researchers led by Pi-
eter Abbeel, an assistant professor of
computer science at the University of
California, Berkeley, programmed a
general-purpose Willow Garage PR2 ro-
bot to fold towels randomly laid down
on a tabletop by using a dense optical
flow algorithm and high-resolution
stereo perception of the towels’ edges
and likely corners. Abbeel’s experiment
yielded a perfect 50-out-of-50-attempt
success rate; the robot was able to recal-
culate failures in the 22 instances that
were not initially successful by dropping
the towel, regrasping a corner, and car-
rying on until the task was completed.

Abbeel says his team has been able
to greatly reduce the amount of time
necessary to fold each towel in subse-
quent experiments, from 25 minutes
to approximately four minutes, by uti-
lizing a new approach: rather than rely
heavily upon onboard perceptual data,
Abbeel has performed parallel compu-
tations on the Amazon cloud on mesh
models. Those models, he says, are
“triangles essentially put together like
people using computer graphics or
physics-based simulations. Once you
have that mesh model, you can do a
simulation of how this article of cloth-
ing would behave depending on where
you pick it up.”
The new approach, he says, relies
on observations that the bottommost
point of any hanging article is usually
a corner. Two consecutive grasps of a
towel, he says, will be highly likely to
yield two diagonally opposed corners.
For t-shirts, he says, likely consecutive
grasps will be at the end of two sleeves
for a long-sleeved shirt or the end of
one sleeve and diagonally across at the
hip for a short-sleeved shirt.

Ros is Boss

Another hallmark advance of the domestic robot community is the growth of an open-source ecosystem, built around the BSD-licensed Robot Operating System (ROS), largely maintained by Willow Garage and Stanford University.

“Our goal has basically been to set
the foundation for a new industry to
start,” Cousins says. “We want two
people to be able to get together in a
garage and get a robotics business off
the ground really quickly. If you have
to build software as well as hardware
from scratch, it’s nearly impossible to
do that.”
Abbeel says the ROS ecosystem may
go a long way to taking the robots out
of the lab and into real-world locations.

“In order for these robots to make
their way into houses and become
commercially viable, there will need
to be some sort of bootstrapping,” Ab-
beel says. “It will be very important
for people to do some applications ex-
tremely well, and there has to be more
than one. So I hope what may be hap-
pening, with robots in different places,
is that different schools will develop a
true sensibility for the robot, and these
things could potentially bootstrap the
process and bring the price down. A
single app won’t be enough.”
Cousins says the combination of
falling hardware prices for devices
such as the PrimeSense sensor, and
the blooming ROS ecosystem might be
analogous to the personal computer
research of the early 1970s, specifically
comparing the PR2 to the iconic Xerox
Alto desktop computer. List price on
the PR2 is $400,000.

Further Reading

Stückler, J. and Behnke, S. Improving people awareness of service robots by semantic scene knowledge, Proceedings of RoboCup International Symposium, Singapore, June 25, 2010.

Maitin-Shepard, J., Cusumano-Towner, M., Lei, J., and Abbeel, P. Cloth grasp point detection based on multiple-view geometric cues with application to robot towel folding, 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, May 3–8, 2010. Schuster, M.J., Okerman, J., Nguyen, H., Rehg, J.M., and Kemp, C.C. Perceiving clutter and surfaces for object placement in indoor environments, 2010 IEEE-RAS International Conference on humanoid Robots, nashville, Tn, Dec. 6–8, 2010. Yamazaki, A., Yamazaki, K., Burdelski, M., Kuno, Y., and Fukushima, M. Coordination of verbal and non-verbal actions in human–robot interaction at museums and exhibitions, Journal of Pragmatics 42, 9, Sept. 2010.

Attamimi, M., Mizutani, A., Nakamura, T., Sugiura, K., Nagai, T., Iwahashi, N., Okada, H., and Omori, T. Learning novel objects using out-of-vocabulary word segmentation and object extraction for home assistant robots, 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, May 3–8, 2010.

Gregory Goth is an oakville, CT-based writer who specializes in science and technology.