GoKawiilFreddo's skills were honed in a virtual environment, where a task can be performed in a computer simulation millions of times. Once the optimum solution (known as a policy) is found, it can be uploaded and used by the hardware - in this case Freddo.
Such virtual simulations are a common way to train robots. Tech giant Nvidia has a system called Isaac Sim which works that way - Lu and Storey both worked on an early version of it.
In 2022 they decided to set up Vsim, to build the their own training system environment and other tools.
As they were starting from scratch Lu and Storey could optimise the software to exploit the powerful computer chips used in AI, known as graphics processing units or GPUs.
"The underlying algorithms that we were using for most of these robotic simulations they hark back to the 1970s and 1980s, but those algorithms are not really brilliant fits for GPUs," Storey says.
Within months they realised their system could work much faster than anything they had seen before.
"Eighteen months in and we actually have a completely functional, super high-performance simulator," says Lu.
The software is so efficient that it can run on the hardware carried by Freddo. That means the robot can run tens of thousand of simulations while it is moving around.
"It can look about a second, or so, ahead into the future for 20,000 different kind of combinations of things that might happen," Storey explains.
And that would be vital for a robot moving around an unstructured environment like the average home.
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