Jovan Aurelio Hartono
CO-FOUNDER
Robotics Lead
and Business Development.
No . . .
This is not sci-fi.
This complex interface is what robotics engineers have to deal with every day.
AUREVAS
Your robot is only as good as its training.
Creating the first plug-and-play platform to train your robots as simple as possible.
NVIDIA Isaac Sim
NVIDIA Isaac Sim UI
Simulation training is going to be the standard in robotics.
But simulators like NVIDIA Omniverse are ridiculously hard to set up and use.
But why should it be?
Researchers, engineers and developers have to build and work with digital environments that are already hard to start with for any project.
Then they have to train the robots with reinforcement learning (RL) that is fragile and difficult to tune right.
That is why R&D and robotics training still take months, high costs and lots of compute.
Something needs to be done to solve this.
Our solution
An AI copilot that sits on top of existing simulators that automates and monitors the entire process from start to end.
illustration
Making robotics training intuitive.
simple and easy as it should be in this era.
The Aurevas copilot sits on top of the complex Isaac Sim. You only interact with the copilot. It’s that simple.
Aurevas copilot will take you through everything.
From setting up digital twins and environment to helping you orchestrate the training specifics until sim to real deployment.
So you can focus on the cool stuff when building your robots.
illustration
But that’s not all...
RL training is still fragile and hard to train.
Reinforcement Learning is the foundation for advanced robotics.
Disney Research
Disney Research
Disney ResearchBut reward tuning and reward function design remain as the biggest bottlenecks in development and scaling.
So we spent a long time to research and test how we can solve this major issue...
And we successfully created a method to do so!
click to view our published paper in IEEE ↗83.3%
Our copilot increases the robot's learning adaptability rate by 83.3%
48.7%
We achieve 48.7% higher training performance than standard commercial robotics pipelines out of the box.
83%
We replaced blind, random AI trial-and-error with an 83% predictability rate in task execution.
Sim to real
Sim to real.
Recognition
Published, awarded, and already in conversation with the people who build robots.
IEEE Xplore (peer reviewed paper)Published · IEEE ↗
Singapore Scientific Conference 2025Published ↗
Four international conferencesPresented
Singapore Science & Engineering Fair 2026Gold Award
A*STAR ARTC & SIMTechInnovative Coding Award
NTU Nanyang Research ProgrammeGold Award
Leading robotics firmsIn talks about potential collaboration
Physical AI organizationsExploring industry collaboration
And we have plenty more ideas that we are currently researching and testing.
+ future worksTeam
Founders
Parith Avasadanond
CO-FOUNDER
Research Lead
and Implementation Machine Learning Engineer
A science-and-research based approach, simplified and commercialized so that everyone can use it.
Direction.
We are looking for the four things that turn a published method into a platform people can use.
01
Mentorship & space
Technical advisors in robotics and reinforcement learning, and a physical place to build.
02
Pilot partners
Labs and teams willing to run real tasks through Aurevas and tell us exactly where it breaks.
03
Hardware access
Arms, humanoids and compute, to close the sim-to-real loop on real robots rather than in theory.
04
People
Engineers who want to build the tooling layer for physical AI alongside us.
AI robotics is inevitable. We are building the platform that lets everyone have a piece of it.
jovan@aurevaslabs.com
parith@aurevaslabs.com
View the intro deck ↗