Hi, I’m Yi-Chi
a Ph.D. student from Aalto university.
My topic is computational workflows for designing input devices.
Designing an input device is challenging.
The motivation of my thesis is searching for better design methods
that offer higher efficiency and robustness.
The traditional procedures of designing input devices
are more or less following the user-centered design flow,
which typically contains design, implement and analyze stages.
And a good design is improved throughout iterations to these steps.
This user-centered design has some major issues.
First, building a new input device
for each iteration is costly.
Second, the design and analyze stages
are highly depending on the designer’s
experience and expertise,
so there is a chance they are biased.
My thesis is trying to offer alternative solutions.
Recently, computational interaction has become
an increasingly important trend in HCI
that offers leveraging optimization and user models
to derive optimal designs
successful examples include:
better user models,
using optimization for designing tasks,
adaptive user interfaces,
and AI-assisted tools.
However, these methods and tools
for GUI-based interaction
are hard to be applied for designing input devices.
Here is why.
Designing input device should consider models
of complex and continuous user motions,
which are not introduced to HCI yet.
Also, rendering arbitrary properties
of physical input devices remains challenging.
Lastly, the optimization methods in HCI
are mostly single-objective,
but a good design usually considers multiple objectives.
Therefore, I propose computational input design workflows.
The key components include:
multi-objective bayesian optimization
which searches for the Pareto-optimal design instances.
and physical simulators
to render the design sampled by the optimization.
Lastly, a meta-reinforcement learning user model
will adapt to a new design instance quickly
and provide evaluation performances.
So far, my research has covered
the optimization and simulation parts.
The first work of mine, published in CHI 2020,
is about push-button simulation and design.
I developed a novel model to describe
the characteristics of buttons,
and further implemented an end-to-end simulation pipeline
that covers from capturing a button
to rendering its haptic properties.
Six buttons are tested,
and the user study shows our simulation outperform the previous works.
The second paper is aiming at
using multi-objective Bayesian optimization
to improve the design of interaction techniques.
Prior works in HCI used single-objective optimization
to assist interaction development,
which forces the developer to merge
several objective metrics into one.
Which is not ideal,
especially, not ideal
for objectives that compete with each other
The major benefit of multi-objective optimization is that
it results in a series of designs
on the Pareto frontier.
This gives higher flexibility to the designers.
This work is currently under review of ToCHI 2021.
The project I’m working on right now
is a meta-reinforcement learning-based agent.
Ideally, it will be able to adapt
to new design instances quickly
by leveraging the previously learned experiences.
That is going to be the last component of this CID framework.
But there are many other interesting research questions ahead.
For example, sim-to-real transfer algorithms,
and more efficient optimization methods,
also, advanced controlling methods for higher precision simulation,
and building simulators beyond push-buttons.
These are all open and challenging research questions.
To sum up,
my thesis contributes the computational input design workflows,
and I further identified and implemented
three major components ranging from optimization,
physical simulation, and meta-reinforcement learning.
I hope this thesis can bring physical interaction
a step toward computational-oriented design processes.
That is the end of my talk.
And thank you for your attention.