Hi everyone, so this is a presentation about modernizing
Rostos skills.
I'm here in Focicale.
I'm the sexual well-engineered at University College London.
I'm presenting this go on behalf of the Advanced Society
Competition and the Geomatic Engineering teams.
So mainly the challenge is how we teach all these
like computer and hardware skills to students.
We need to manage lots of things,
the server, the virtual machines, the robot sensors,
and the way we are trying to do this is by using cloud computing.
And the reason is because participants
or persons have different skills and levels,
so we need to handle all these kind of dependencies
of making sure they strive for where to start the developing.
So what we are trying to do is orchestrating a few of the infrastructure.
Like a bare metal server that runs all the packages
that collect the sensor data, control the robots,
and we use a signal router to communicate through
Rostos middle-guests through these virtual machines.
So this metal machine is making easier for users to start
connecting to them via SSH and start playing with
Rostos packages, Rostos Playbacks.
They really start working on those kind of life fun stuff.
And not really struggling,
I need to install my Python version 3.12,
3.11, 3.12 and playing with all these flavors of Rostos.
And everything, which is also fun for me,
but not maybe for everyone.
So that's why we are kind of playing with this infrastructure.
So this is like for you kind of like a demos.
We have been doing this kind of workflow
where we develop the Docker containers with all the Rostos dependencies
with them, create the GitHub container registry,
which is make it easier to make it portable.
And then that way we pull that image into the virtual machine
and that's more easier for users to start
just playing with the data.
So that's what we are doing now.
We have done this virtual and virtual
and virtual metal communications.
We are this and uploading Rostos DS.
Also the other thing, we are playing around
is how we interact with GPU, server capacities.
So for example, the ISAXINA and ISA lab.
So we have been running this into the virtual machine.
Just the users, just connect to SSH.
And then start playing with the interaction
and all the kind of the models.
So lots of things that we want to do
in the future, like we more of the multi-modal sensing
and going from the low level to the high level attention.
So there's a lot of things that depends on the sensor we focus on.
And of course, we are kind of also developing, sorry about this.
But we are working on the, I don't know what's happening.
But one thing we are working on is developing visual language models.
And action models are also the visual language navigation models.
Sorry about this.
But that's what we are trying to do.
That's kind of hit a wall.
The only thing we are working on is on healthcare
and robotics, medical robotics applications.
So we've been organizing workshops.
The handling medical symposium.
So last year, we have like 50 participants,
10 speakers, these posters, and Ben was all out.
And we are organizing a new event.
If anyone is interested in around these topics, you just talk to me.
And lastly, we have like a few takeaways.
What we want to do is lower in the barriers to advanced robotics
as a scale.
We have a proven and scale model for collaboration.
We have a test bed for research training and innovation.
And of course, if anyone is keen to work with us,
or collaborate, please talk to me or Marlon who is also in the audience.
Yeah, thank you.
And to all the colleagues from UCL and the panelists.
And yeah, we have everything on GitHub as well on the website as well.
So yeah, thank you very much.
Thank you so much Miguel.