What Robotics
Can Learn from HCI
Aaron Powers
iRobot | apowers@irobot.com
As the robotics field grows and
becomes competitive, robotics companies are beginning
to inject user-centered design
methods into their processes.
Applying HRI methods to industrial and commercial products
introduces new challenges and a
focus on cheap, proven methods.
The specialty of human-robot
interaction (HRI) is a growing
group of roboticists, social scientists, and designers, but the
field of industrial practitioners
is still small. Robotics has yet
to reach the transition point
that Don Norman talks about in
The Invisible Computer, where the
level of performance exceeds
users’ needs [ 1]. For that reason,
the robotics industry to this
point has focused on technology
rather than user experience. As
we see robots become ubiquitous
consumer products, that focus is
starting to change.
At iRobot we have one practitioner of HRI (that’s me). iRobot
has begun the transition from
a technology-centered company
to a user-centered company, as
we grow from research robots
toward commercial products. As
we focus more on product development, we transform many
research methods from the HCI
and HRI fields into practice.
Additionally, robotics companies
provide a good opportunity to
put HCI principles into practice.
Because robotics companies
like iRobot are growing quickly
and shifting toward commercial
products, the field is too new
to have an ingrained process.
HRI can become the framework
for development of commercial
robots.
Ethnography is the most
popular investigative method
being adopted in commercial
HRI. Detailed ethnography studies helped iRobot learn about
the culture of the PackBot users
in the military and about the
homes and cleaning patterns
of Roomba owners. The open-ended approach of ethnography
allows a series of short studies
to explore varied topics and
build a baseline of knowledge.
Interacting with a humanlike
robot is, in some ways, like the
intersection of a new culture
into an old one, making ethnography an excellent method
of research and evaluation. For
example, you may have seen a
Roomba push an empty trash
can around the room or catch
computer cables as it vacuumed.
Environment and context can be
crucial factors in the success of
a robot, and the ethnographic
method is effective at discovering their influence.
Certainly, we run experiments in industrial HRI, but running formal experimentation is
rare. It is much more common
that we need quick, effective,
“discount” techniques because
projects or decisions are often
on a tight deadline. Just as “
discount” techniques have garnered
widespread commercial use in
the usability domain, they are
needed in HRI as well.
To understand what principles
of HCI will have the most impact
in HRI, iRobot ran a series of
systematic evaluations of several
of iRobot’s teleoperated robots,
which are driven by remote control. iRobot has several teleoperated robots, such as the PackBot,
the R-Gator, and the recently
announced ConnectR. To study
teleoperation, we collected many
hours of observations and documented more than 700 one-line
“stories” from the observations.
For example, users commented
that powering the robot through
remote control was difficult
because their vision was limited.
The video stream that users
use to drive the robot had a low
frame rate and lagged by less
than a second. We’re using these
stories to identify issues and prototype new ideas. By watching
the videos, we noticed that when
a team is working with a robot,
they would often point where
they were going to before they
would drive there. So we prototyped a laser-pointer robot—
operators use the laser pointer to
put a dot on the ground in front
[ 1] Norman, D. The
Invisible Computer,
Cambridge, MA: MIT
Press , 1998.
