Welcome to our presentation on iteratively adapting avatars using task-integrated optimisation.
This work is done by myself, Jess Mcintosh
and my colleagues, Hubert Zajac, Andreea Stefan,
Joanna Bergström and Kasper Hornbæk
Avatars represent the user in virtual reality.
They have form, which usually mimics the outline of a human body.
The user embodies the avatar, and they are able to control it.
But there are many ways in which we can adapt an avatar.
By adaptation, I mean changing the shape of the avatar in some form
such as scaling an avatar, or stretching the arms
as shown in these two examples.
There are many cases where adaptations can be beneficial
such as walking faster, or increasing your reaching distance
again investigated in these two works.
But for a given adaptation, how do we best choose
the right amount of adaptation, or the right parameters,
that best fit a certain task or environment?
To this end, we propose iterative task-integrated optimisation
a generic approach to finding the best adaptation
for a particular task or environment.
And this is how it works...
We first start with a baseline avatar
Let them perform the task with the avatar,
then assess it’s fitness, which is a function
that can be comprised of performance, or other measures
We feed the data from the fitness function into
optimisation algorithm, which then adapts the avatar...
Which the user then tests again in the same task
and this cycle of adaptation and task assessment continues
Until we reach a finishing criteria,
and an avatar is selected
Let’s go through an example.
There are many instances of past work
where the forearm of the avatar is adapted.
But past work either chooses it arbitrarily
or try a few lengths within a range.
So naturally, we studied our approach on this.
Our baseline avatar is simply an avatar that matches the users body
As a task, we had a spiral of targets
and the user would have to reach and tap
the highlighted target,
which sometimes required walking.
For performance measures, we used
throughput and body movement. Then
we calculated the fitness as a function of these.
We used an algorithm similar to gradient descent
to adapt the avatar based on the fitness delta.
Then after 30 iterations, we selected the avatar
with the highest fitness as the optimal.
In our study, we compared the baseline to the optimal avatar
The results showed that the overall performance
favoured the optimal avatar
the Nasa TLX also showed that mental and physical demand
were lower in the optimal avatar.
Looking a bit closer at the performance,
The distances moved by each body part
was overall lower for the optimal avatar.
And the task time and throughput
were both significantly improved.
We wondered if this approach could be generalised.
So we took a different task,
adaptation, and optimisation algorithm.
Here, the task is similar but with a different layout.
The adaptation is instead finger scale,
and we also used a bayesian optimiser.
Here you can see an example of a baseline hand
versus an optimised version with very long fingers
and although it might look quite strange,
they are useful since they allow the user
to tap targets with minimal hand movement.
and similar to the first study,
the optimised finger scale performed
significantly better than the baseline.
In future work, we think that the approach can be
extended to include other measures in the fitness function
such as ownership, presence or other experience measures.
Body morphology can also be a parameter for adaptation,
such as the number of arms or fingers.
Thanks for listening,
I’m happy to take any questions you may have.