Google's Adorable AI Robots Show Us The Future of Robotics... and Sports?

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For all the impressive features that ChatGPT, Stable Diffusion, or DINO have exposed to the world, they all have one common “limitation”:

They are bounded to the digital world, with no possibility to impact the physical realm… at least not explicitly.

But now we’re one step closer to achieving one of the grand dreams of AI scientists:

The development of embodied intelligence

That is, the creation of agents that can act in the physical world with the agility, dexterity, and understanding of animals and humans.

In the not-so-distant future, this could not only fully automate all our production lines, but also take up the form of robot assistants, robot soldiers… or even robot lovers.

“Robot in the style of Hito Steyerl” Source: Author with Diffusion model

And the latest demonstration of potential comes with soccer (or football as we call it in Europe) robots that will make you smile, laugh, and also reflect on how society is not prepared, in the slightest, for what’s coming.

The Fascinating Concept of ‘Rewards’

The main characters of our story are sports robots trained using what we describe as Deep Reinforcement Learning, or Deep RL.

In a nutshell, this is the idea of using neural networks, that same concept behind ChatGPT, Dolly, or even Tesla’s Autopilot system, but applied to RL.

But what is RL?

While NLP models like ChatGPT work by processing natural language and generating a textual or image response, and CV models like SAM learn to process images and identify elements and infer patterns from them, RL teaches AI agents to interact with their environment.

In layman’s terms, they learn the best way to execute the set of actions that will yield the maximum reward possible.

To further understand, you can think of training RL agents as training your dog puppy.

By rewarding him/her with treats for good behavior and “punishing” without them otherwise, the dog learns the pattern of actions (the policy) that will yield him/her the greatest amount of treats (rewards) possible.

But how do we teach robots to play sports?

It’s all about winning

As you know, the aforementioned sport is not only about kicking a ball; the robot needs to learn to get up, move, shoot, and, in 1v1 situations, defend its goal.

Hence, the DeepMind team assembled a set of rewards that would allow the robots to learn to play it by:

Rewarding it for scoring, getting up, and defending

Penalizing it when receiving goals, or staying on the ground

To put this into practice, Google DeepMind leveraged a very powerful concept that’s gaining a lot of traction recently in the world of AI, distillation.

Like Father, Like Son

The researchers first trained two robots:

A scoring teacher, a robot that learns to score goals effectively

A get-up teacher, a robot that learns to get up from different poses effectively

Next, they should this process of distillation, which is getting a student robot to learn from them.

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