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Companies / Google / Google’s robot takes on humans in table tennis
CompaniesGoogleResearch

Google’s robot takes on humans in table tennis

AI-powered arm serves up amateur-level competition

Daniel Reed
ByDaniel Reed
AI Research, Safety & Ethics Analyst
Daniel Reed currently works as an AI Research, Safety & Ethics Analyst at Aiholics, writing about how changes in artificial intelligence are affecting and will affect...
- AI Research, Safety & Ethics Analyst
Published: August 9, 2024
5 Min Read
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The DeepMind team from Google developed a ping-pong-playing machine that is almost as good as an amateur human, which is quite a milestone for robotics and artificial intelligence. With its smart AI, this robotic arm can rival humans playing in real-time by changing strategy during the game and even beating them in some cases.

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Key points

  • Google DeepMind created a robot arm that plays table tennis at an amateur human level.
  • The robot won 45% of its matches against human players of varying skill levels.
  • It uses a combination of specific skills and strategic decision-making powered by AI.
  • The AI was trained using a hybrid approach of computer simulations and real-world gameplay data.
  • This research has implications beyond table tennis, potentially impacting various fields where robots need to interact with humans.

This robot consists of a mechanical arm mounted on tracks to allow it to move around freely. It has high-speed cameras that keep track of the ball and the player it’s facing. The uniqueness of this robot lies in its “brain” –an advanced AI system that combines particular table tennis skills with decision making during gameplay.

Coach Barney demonstrates capabilities. Source: Google DeepMind

In tests, 29 humans players were put against the robot at different levels of skill. Against beginners, it won all matches and 55% against intermediaries but was beaten by advanced players in all matches. In general, the robot managed to win 45% games proving its being useful for non-professional purposes.

Truly awesome to watch the robot play players of all levels and styles. Going in our aim was to have the robot be at an intermediate level. Amazingly it did just that, all the hard work paid off. I feel the robot exceeded even my expectations. It was a true honor and pleasure to be a part of this research. I have learned so much and am very thankful for everyone I had the pleasure of working with on this.

Barney J. Reed, Professional Table Tennis Coach

Most interestingly, how did they train AI? Some computer simulations were combined with real-world data by researchers. They would start small with some tidbits about human play then let loose the robot to battle with actual people. Any new match provided more data which was fed back into simulation so as to improve further training. This process was repeated many times allowing the machine to become better and stronger adapting itself across various playing styles.

Strangely enough, even those who lost still enjoyed playing against this kind of robot. On another note; many considered it fun or enjoyable; thus indicating possible usefuless of AI in sports practice and amusement parks among others Nevertheless however, there are still some flaws within our bot: it doesn’t handle really fast or high balls well; it finds intense spin difficult to read; and is weaker at backhand strokes.

However, their implication does not only revolve around table tennis but also cover wide range of robotic works requiring fast responses and adaptation to the uncertain human behavior. For instance, this could be used in manufacturing industries, healthcare among others where robots should interact with people with skill and safety.

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Match highlights. Source: Google DeepMind

While this is a remarkable achievement for a robot, its creators admit that it is only a step towards creating many different kinds of useful real-world robots that can do things as well as humans. There is still much progress required to reach human level performance across tasks and also develop robots that are safe and efficient in daily human settings.

In general, projects like this table tennis robot help to push the boundaries of what can be achieved as robotics and AI continue advancing, bringing us closer to an age when machines can assist or talk with humans more intelligently.

TAGGED:AIDeepMindGoogle
SOURCES:Google DeepMind

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ByDaniel Reed
AI Research, Safety & Ethics Analyst
Daniel Reed currently works as an AI Research, Safety & Ethics Analyst at Aiholics, writing about how changes in artificial intelligence are affecting and will affect scholarship, society, and human civilization. He reports on breakthroughs in AI research, the development of safety frameworks, discussion of long-term risks, and ethical challenges; he also reports on global shifts in policy and governance. Daniel aims to make complex research papers and long-term thinking accessible to the everyday reader without sacrificing nuance. With his thoughtful and analytical style of writing, Daniel translates advanced topics into clear language. He targets questions that really matter: how safe are today's AI systems, what kind of ethical boundaries do we need, and how could exponential progress affect the way education, jobs, governance, and human values are shaped? His articles are often not just expert opinions but also balanced views and insight into emerging debates that define AI's place in the world. Daniel believes responsible AI development begins with awareness, transparency, and informed public conversation. In terms of his work with Aiholics, he encourages readers to look beyond headlines to understand the promise of artificial intelligence but also some of its consequences.
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