Robots Lending a Helping Hand
Allison Bruce graduated from CMU in 2000
with a BS in computer science. Her undergraduate research involved creating
architecture for dramatic improvisation between two mobile robots (Robot
Improv). She is now a graduate student at the Robotics Institute, advised by
Reid Simmons and Illah Nourbakhsh. Her current research project is the Social
Robots Project. Her research interests involve the application of machine
learning and planning techniques to the problem of human-robot interaction.
is involved in Women@SCS and the Pittsburgh chapter of Computer Professionals for Social Responsibility. In her free time, she enjoys reading, watching films,
and attending local arts events.
How did you become interested in robotics?
Allison: I was in Wean
one day and saw a poster for the Intro to Mobot Programming Lab class. The
poster said, "Do you not have enough robots in your life?" And so I
thought, "well, maybe I don't." So I decided to take the course and
got totally obsessed with it. I spent tons and tons of time hacking on robots
at 3AM and watching them crash through cardboard walls. There is something very
satisfying about it. I really like programming something and then watching it
move. It's so much more engaging that way and I find that very rewarding.
How did you become interested in social robots
Allison: I worked on
an undergraduate research project called Robot Improv. This basically consisted
of two robots improvising short plays. It was pretty goofy. Surprisingly,
people actually enjoyed watching it (even though it was basically these two
little robots that looked like trash cans chasing each other around). The fact
that people actually showed interest in interacting with robots gave me the
idea to pursue social robots.
What were some of the main motivations for your social
Allison: A lot of
fundamental problems in robotics are starting to be solved. For example,
navigation in a known environment, given a good map of the environment is
becoming a solved problem. This changes the way we can potentially use robots.
What we want to look at now is how we can make robots that people can interact
with. People who are not researchers or people that actually program the
robots, but just regular people that might need a robot to help them with their
What are some examples of day-to-day tasks that might require
help from a robot?
Allison: You can well
imagine a robot doing some sort of menial work in an office or in a hospital.
Environments like these have lots of people around. Because of that, the robot
must have some sort of social competence in order to not be an obstacle to
people doing what they need to do during the course of the day. For example,
you want the robot to have an understanding of the social rules that people use
to regulate their behavior in crowds. You want a robot to move down a hallway
the way that people do, staying on side, and passing only when they should
pass; not swerving around people as if they were obstacles and disrupting
things. We are interested in whether or not we can encode these social rules or
ideally, have the robot learn these social rules through interacting with
people and gathering data.
What type of robot are you using for this project?
Allison: I'm currently
working with a Real World Interface B-21. It's a typical research robot.
Robots are often looked upon as strange and unusual
entities, and are sometimes even feared by the general public. How are you
accounting for this when designing your robots?
Allison: Even though
robots aren't humanoid looking at all, people tend to anthropomorphize anything
that they interact with that moves. So we're trying to support that
anthropomorphism to a degree. Our robots have a humanoid face. It's basically a
computer model that is displayed on a screen that is mounted on the robot. The
face is something that is familiar to people. We are interested in, not just
having the face there because it's cute and makes the robot seem friendlier,
but also because people monitor each other facial expressions to get an
indication of how that person is doing in an interaction. So we want to use
facial expressions as a mechanism to convey information to people about what
the robot is doing or how the robot thinks it's accomplishing its task.
Can the robot actually communicate with people?
Allison: It does to a
certain degree. Our robots can execute scripts where it speaks certain
dialogues. And it moves through these scripts using a finite state machine to
define its behavior. It transitions through this finite state machine based on
perception. Right now, we don't have any dialogue capabilities because we do
not have speech recognition on the robot. However, one of our short-term future
goals is to work on a limited form of speech interactivity where the robot will
be able to use keyword matching to get responses from a person.
Have you field-tested your robot?
Allison: We did one
large experiment, looking into something very fundamental. And that is that
people have a common idea that robots should seem more human to improve
interaction. Robots should have a face and be capable of expressive movement
and behavior. But no one has actually tested this idea in a rigorous way. So we
designed a psychology experiment with two variables. One was whether the robot
had a face or not and the other was whether it would turn towards the person
and focus its attention on the person when talking with them or not. The robot
performed the social task of asking passersby a poll question. We measured the
robots success at this task when we manipulated those two variables. We found
that having the face and movement individually as well as together improved the
level of success. Each of the variables were important in making the robot seem
more lifelike, therefore people like it better, and want to interact with it
What aspects of this project are you currently trying to
Allison: Right now,
I'm trying to get the robot to recognize a person's intentions and use that to
respond more intelligently. For example, in the last experiment, the robot
would say "Hello" to everyone that passed by. However, from observing
people as they walked through the door, it was clear that some of the people
were definitely going to talk to the robot, while others were not. So we want
to try and encode something so that the robot can look at a person and guess
whether the person would want to talk to them or not. Then, the robot would
only address the people that it thinks might actually be interested in talking
to them. I'm using some machine-learning techniques to try to learn models of
those two behaviors and allow the robot to distinguish between them.