Hello everybody, my name is David Porfirio, and today I'm going to be presenting the work that I did with my collaborators
which we call Figaro: A Tabletop Authoring Environment for Human-Robot Interaction.
Essentially, the Figaro interface enables interaction designers,
or demonstrators, as we call them,
to program the flow of mobile human-robot interactions.
In describing Figaro in more detail, I'll first talk about our motivation for constructing it,
followed by a description of the system, and ending with our evaluation.
Let's start with our motivation.
So first we were inspired by shadow puppetry, and how you can create complex behaviors with minimally-instrumented figures.
We were also inspired by our own successes in bodystorming human-robot interactions,
and how we can synthesize full programs just from designer demonstrations.
We wanted to draw from the strengths of shadow puppetry, and ultimately create a new way of demonstrating human-robot interaction flows,
based on manipulating figurines.
I'll now move on to how we constructed Figaro to meet these goals.
Figaro is comprised of three main components: a robot,
a tabletop interface which demonstrators use to manipulate the figurines,
and when using the tabletop, a tablet, which demonstrators use to progress through the various stages of the design pipeline.
There are three high-level design stages, starting with using the tablet to draw a layout.
So when drawing a layout, demonstrators create a virtual representation of the environment,
within which a robot will perform a task or have a social interaction.
And after a layout is drawn, demonstrators will iteratively perform scenes on the tabletop with the figurines.
To start, the layout uploads to the tabletop.
Then, both figurines can be placed on the tabletop, one of which represents the human,
and the other which represents the robot.
Then the tabletop will sense various actions performed by the demonstrators as they manipulate the figurines.
For instance, the tabletop tracks the position of both figures.
Figaro also records demonstrator speech.
"Human say: Hi robot, how are you doing?"
Furthermore, the robot figurine is instrumented with a joystick base,
meaning that Figaro can record actions performed on the robot figurine itself.
If the demonstrator pushes the figurine down, tilts the figurine in a particular direction,
or both tilts it and pushes down at the same time,
these behaviors will translate to the robot waiting for further input, pointing in a particular location, and nodding its head.
Demonstrators then manipulate the figurines to act out scenes of an interaction,
or in other words, positive input-output examples that demonstrate how a robot should behave.
"Robot, I need to make a call."
Figaro records the scene as an execution trace.
"Human say: could you please tilt your head back?"
Demonstrating multiple scenes shows the robot how it should behave under varying circumstances.
In each scene, the demonstrator does something different.
Given multiple execution traces,
Figaro will then employ program synthesis to assemble a full automaton that accepts each execution trace.
The full program can then be deployed on a robot for execution.
This is an example deployment in which a robot provides help to visitors to a museum.
The red dot shows where the robot is in the execution of the program.
After we designed Figaro, we evaluated it with a ten-person validation study.
In this evaluation, each participant was guided through a tutorial about how to use Figaro.
Then they were asked to design a human-robot interaction at a museum,
in which a museum-guide robot answers people's questions, and guides people to different exhibits.
We found that most participants took advantage of the tangibility of the interface, moving figurines around, and using the joysticks.
We extracted various themes from our post-task interviews with participants.
Some notable themes include that Figaro served as a means of idea generation for participants,
for instance, by grounding them in a more realistic design space,
and imposing physical restrictions on the types of actions that the robot can perform.
As another example, participants found the instrumented figurines to add expressiveness to the design process,
while also expressing hesitation in taking advantage of the tangibility.
To conclude, we've demonstrated what Figaro is capable of, including its expressiveness.
Additionally, our evaluations validate the intuitiveness of demonstrations and the benefits of tangibility.
In future work, we must increase support for debugging and testing,
add support for more detailed environments on the tabletop,
increase the instrumentation of the figurines,
and also improve the synthesizer's ability to generalize to unseen demonstrations.
Thank you for your time, and I look forward to answering your questions.