Robotics: Firms race to improve robot training systems

Rika Antonova has been working in the field of robotics since 2015 and is currently an associate professor at the Department of Computer Science and Technology at the University of Cambridge.

Her research is focused on, external developing software and hardware that can aid robots to learn complex behaviour.

Antonova works with a training system called MuJoCo, owned by Google’s DeepMind since 2021. It’s open-source software, which means researchers can use it for free, and are allowed to tinker with the code.

“It is very, very user-friendly. So for research groups or for small start-ups, that’s useful,” she says.

She says that Vsim’s approach – very fast simulation – is promising.

“If you have a very, very fast simulator, then you can simulate hundreds of millions of samples in that few seconds that your robot is thinking about how to adjust its motion, and then you can change the motion almost in real time,” she says.

But those simulated environments are still rough approximations of the real world, which limits what can be trained.

“There are certain things that are hard to model in simulation, like highly deformable objects and cutting,” she says.

It’s a challenge that Nvidia and Lu and Storey at Vsim are working on.

Lu says their system has “reduced approximation, using accurate simulations to train models that genuinely work in reality as well as they do in simulations.”

Soon a second robot, to be called Nacho, will be helping develop that tech.

Lu says that should speed up their development process and ensure their software can run on different machines.

And, of course, provide Freddo with some company.