Hot off the Wires 🔥

We're eavesdropping on the smartest minds in research. 🤫 Don't miss out on what they're cooking up! In this section, we dissect some of the juiciest tech research that holds the key to what's next in tech.⚡

As the dust settles on the epic 2024 Summer Olympics, you might think the world of sports is taking a breather. Think again! While human athletes recover from their medal-winning feats, a new challenger has stepped up to the table – quite literally.

Researchers at DeepMind have created a robotic table tennis player that's giving human amateurs a run for their money. Yes, you read that right! A robot that’s achieving human-level competence in playing table tennis, and it might just be the most thrilling development in sports tech yet.

Forging the Fundamentals

Before we dive into the nitty-gritty, let's break down some key terms:

So, what’s new?

Robotics has come a long way—from Boston Dynamics' robots performing parkour to those that can cook, clean, and even assist in surgeries.

But mastering the lightning-fast, strategy-packed world of table tennis? That's a whole new ball game. Table tennis isn't just a fun pastime – it's a treasure trove for advancing robotic capabilities. Here's why:

• It requires complex motion and split-second hand-eye coordination

• Players need high-level strategies that adapt to opponents' strengths and weaknesses

• Success depends on both strategic decision-making & physical execution (unlike purely mental games like chess)

Sure, there are "robots" in table tennis today, but most are glorified ball launchers. Some more advanced models can return balls, hit targets, or engage in cooperative rallies. But a full, competitive match against a human? That's been the stuff of science fiction – until now.

DeepMind's latest creation is the first learned robot agent to achieve amateur human-level performance in competitive table tennis. Here's what makes it special:

• A robotic arm with 6 degrees of freedom (3 for translation, 3 for rotation), sporting a 3D-printed paddle

• Two cameras to track the ball's position

• A unique approach combining the best of reinforcement learning & imitation learning

• The ability to adapt on the fly to human opponents' playing styles

Under the hood…

This robotic table tennis player isn’t just a product of raw computational power. It’s the result of an intelligent blend of reinforcement learning (RL) and imitation learning (IL). The researchers alternated between training the robot in a simulated environment (RL) and then deploying it in the real world (IL), where it could play against human opponents without any additional tweaking—a process known as zero-shot deployment.

Table tennis gameplay is modeled as a Markov Decision Process (MDP), where each ball hit and returned is treated as an individual episode. The secret sauce to using this model effectively here is a hierarchical control system:

• Low Level Controllers (LLCs): Instead of one monolithic controller, 17 specialized LLCs were trained to handle various aspects of the game, like targeting specific return locations or maximizing return velocity. This modular approach prevents catastrophic forgetting (where the robot might forget how to perform a skill after learning a new one) and allows for quick decision-making.

• High Level Controller (HLC): The HLC is the mastermind that decides which LLC to activate in response to each incoming ball. Within just 20 milliseconds, it assesses multiple factors to decide on the best LLC to choose for an episode. The HLC’s decision-making process is powered by a combination of convolutional neural networks (CNNs) that first decides whether to play a ball using forehand or backhand, along with a custom spin classifier & an algorithm that updates the preference of each LLC by adapting to the opponent’s playing style during gameplay.

Training the LLCs was another intricate task in itself:

1. The researchers first created a custom table tennis simulation environment using the physics engine MuJoCo

2. They used the Blackbox Gradient Sensing (BGS) algorithm to train generalist policies for forehand & backhand separately

3. Each of these generalist policies was refined into several specialist LLCs based on advice from table tennis coaches—a perfect example of how human expertise can enhance AI development

4. If these specialized LLCs performed as expected during evaluation, they were retained, else discarded

But simulation can only go so far. When deployed in the real world, some corrections were needed, particularly for dealing with topspin and underspin. To address this, the researchers added ‘adapters’ to the LLCs, ensuring the robot could adapt its learned skills to real-world gameplay.

Why does this matter?

When pitted against 29 human table tennis players of varying skill levels, this robot showed astounding results. Although it struggled against advanced players, it managed to crush beginners (100% win rate) and held its own against intermediate players (55% win rate)!

Don’t believe us? Check out the actual match highlights👇

Not only did the robot demonstrate competitive gameplay, but it also made the experience fun and engaging for human players, which is crucial for wider acceptance and use of such technology.

This isn't just about dominating your local table tennis league (although that would be pretty cool 🤩). Spark & Trouble can’t help but think about the implications of this research stretch beyond the TT table.

• Adaptive Robotics: Imagine industrial robots that can quickly adjust to new tasks or unexpected situations.

• Human-Robot Collaboration: This technology could lead to more intuitive and responsive robotic assistants in healthcare, manufacturing, and even space exploration.

• Sports Training: AI-powered training partners could help athletes analyze and improve their techniques in various sports.

• Rehabilitation: Adaptive robotic systems could provide personalized physical therapy, adjusting difficulty based on a patient's progress.

• Entertainment: Think beyond simple animatronics – we could see truly interactive robotic characters in theme parks or educational settings.

While we might not see robots competing in the Olympics (yet), this table tennis triumph marks a significant milestone in the quest for human-level robotic capabilities. So, the next time you're looking for a table tennis partner, don't be surprised if you find yourself facing off against a mechanical maestro.

Wish to dive deeper into the technicalities of this innovation? Head over to their research paper to know the specifics.

Key Takeaways

(Screenshot this!)

In the Spotlight💥

Last week, we had a super interesting chat hosted by Tejj, an award-winning designer & author, on his podcast D’ Industry - where we delved deeper into:

• The basics of AI and LLMs 📖

• The AI landscape in India 🟧⬜🟩

• Exciting research and product developments in India 🔬

• Challenges and opportunities in the Indian AI space ✨

Watch the full episode on YouTube for all the insights 👇

And…don’t forget to share your thoughts in the comments 😊

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Spark 'n' Trouble Shenanigans 😜

Spark & Trouble are finally able to dance!🕺💃 
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