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🏓 Game, Set, Match: AI Serves Up a Table Tennis Revolution! 🤖 Google DeepMind's latest creation is turning heads (and paddles) in the world of robotics and AI. Their new robotic table tennis agent is showing "human-level speed and performance," winning an impressive 45% of matches against players across skill levels! Key points: • 100% win rate vs. beginners • 55% win rate vs. intermediate players • Real-time adaptation to opponents' styles • Combines simulated training with real-world data This isn't just about ping pong – it's a leap from AI mastering virtual games to excelling in complex physical tasks. We're witnessing the dawn of robots that can truly adapt to the real world in real-time. What do you think? Are we ready for robot sports leagues? How might this tech reshape industries beyond gaming? Image source: Google DeepMind Robotics Follow 15minAi for more AI topics! #AI #Robotics #MachineLearning #FutureOfTech

In a remarkable leap forward, Google DeepMind has developed a table tennis robot that showcases the incredible potential of AI and robotics. This AI-powered marvel isn't just playing ping pong—it’s dominating the game, particularly against beginner players, winning 100% of its matches! The robot, equipped with a sophisticated system of high-speed cameras and neural networks, mimics human gameplay with astonishing precision. Its ability to adapt in real time, learn from its opponents, and even exploit weaknesses brings us closer to the day when robots will be commonplace in human domains. Key Highlights: Advanced AI Strategy: The robot uses a dual-system approach combining Low-Level Controllers (LLCs) for specific skills and a High-Level Controller (HLC) for strategic decision-making. Real-Time Adaptation: With continuous learning during gameplay, it adjusts strategies on the fly, enhancing its performance with each match. Impressive Performance: The robot excels against beginners, holds its own against intermediates, and even challenges advanced players. Barney J. Reed, a Table Tennis Coach, praised the achievement, saying, "Amazingly, the robot met our goal of playing at an intermediate level. It was a true honor and pleasure to be part of this research." This development is just one example of how AI and robotics are pushing boundaries, turning science fiction into reality. Whether it's in sports, healthcare, or any other field, the future is bright for AI-powered innovations. 🌟🌐 How do you think AI will impact the future of sports and other human activities? Browse more https://seekme.ai 👇 #AI #Robotics #Innovation #TableTennis #DeepMind #FutureTech

𝗧𝗮𝗯𝗹𝗲 𝘁𝗲𝗻𝗻𝗶𝘀 𝗥𝗼𝗯𝗼𝘁 𝘃𝘀 𝗛𝘂𝗺𝗮𝗻 !!! 👾 So, Google DeepMind just did something really cool in the world of robotics and AI. They built a table tennis robot that can play against humans at a pretty high level, even if it's never seen its opponent before! What's really impressive is how they made it happen: 𝗛𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗰𝗮𝗹 𝗣𝗼𝗹𝗶𝗰𝘆 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: Lets the robot master specific table tennis moves and make strategic decisions. 𝗛𝘆𝗯𝗿𝗶𝗱 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵: Combines human gameplay data, reinforcement learning in simulation, and real-world play to refine the robot's skills. 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗚𝗮𝗺𝗲𝗽𝗹𝗮𝘆: The robot can adapt its gameplay in real-time, tracking match stats and adjusting its strategy based on its opponent's strengths and weaknesses. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝗶𝗺-𝘁𝗼-𝗥𝗲𝗮𝗹 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀: To fine-tune the robot's training and make it more effective in the real world. The result? A super-skilled and flexible robot that's changing the game for robotic sports and human-robot interaction! It shows us that robots can learn complex physical tasks and interact naturally with humans. Check it out: https://lnkd.in/gerM-WHi So, what does this mean for the future of robotic sports and AI in physical tasks? Could we see more human-robot competitions in the future? #DeepMind #AI #Robotics #MachineLearning #TableTennis #Innovation #Google #TechNews #RL

Google DeepMind has made a significant leap in AI and robotics with their new robotic table tennis AI agent. This agent has achieved "human-level speed and performance," winning 45% of matches against players of varying skill levels. Reflecting on my own experiences with AI, it's incredible to see how far we've come. I remember when AI could only handle tasks like playing chess. Now, we're talking about physical games like table tennis! Here are some key points from this development: - The robot won 100% of matches against beginners and 55% against intermediate players across 29 matches. - It uses a blend of simulated training and real-world data to hone its skills. - The system adapts to opponents' playing styles in real-time, adjusting its strategy on the fly. - While it excels against amateur players, it still faces challenges against advanced opponents due to physical and skill limitations. This breakthrough is a step closer to achieving the robotics community's 'north star' of human-level performance in real-world tasks. Imagine the possibilities for robots that can adapt in real-time to the physical world! What are your thoughts on AI's progress in physical tasks? Do you think we'll see more AI in sports and other physical activities soon? #AI #Robotics #DeepMind #Innovation #Technology #ArtificialIntelligence

🏓 Google’s ping-pong-playing robot Google DeepMind has developed a robotic table tennis AI agent that reached “human-level speed and performance,” winning 45% of matches against opponents of varying skill levels. - Match Success: The robot won 100% of matches against beginners and 55% against intermediate players across 29 matches. - Training Method: It uses a blend of simulated training and real-world data to refine its skills. - Adaptive Play: The system can adapt to opponents' playing styles in real time, adjusting its strategy on the fly. - Challenges: While successful against amateurs, the robot still struggles against advanced opponents due to physical and skill limitations. 🤖 Why It Matters: 1. New Era of Physical AI: Competing in physical games like table tennis marks a significant step forward, bringing us closer to the robotics community's goal of human-level performance in real-world tasks. 2. Future Potential: This breakthrough opens up new possibilities for robots that can better adapt to and interact with the physical world in real-time. How do you think this will impact the future of robotics and AI? #googledeepmind #ai #robotics #tabletennis #adaptiveai #realworldai

This is mind blowing 🤯🏓👏🏻 Google DeepMind’s AI-Powered Robot Reaches Amateur Level in Table Tennis! Google DeepMind has developed an AI-powered robot that has achieved amateur human-level performance in table tennis, marking a significant advancement in robotics. The robot uses a hierarchical and modular policy architecture to master both low-level skills, like forehand topspin and backhand targeting, and high-level strategic decision-making. After extensive training in a simulated environment, the robot faced 29 human opponents, achieving a 45% win rate overall and winning 100% of matches against beginner players. Despite these successes, the robot still has some weaknesses, particularly in handling underspin shots. This achievement underscores the rapid progress in AI as it learns to tackle complex, real-world tasks, pushing the boundaries of what’s possible in robotics. Can AI robots beat human players in table tennis? 🤖🏓 Share your thoughts in comments! [Credit: Google Deepmind] #ai #artificialintelligence #openai #machinelearning

Google DeepMind's latest breakthrough trains robots to kick, tackle, and defend, showing off impressive soccer skills. Engineers and AI experts from DeepMind have successfully developed robotic athletes that could change how sports are played and coached. Watch the video of these robotic athletes playing soccer ⬇️ #AI #DeepMind #Robotics #AISports #FutureOfSports #TechInnovation #ArtificialIntelligence #Soccer https://lnkd.in/dJ2vCMDP

🚀 Breaking New Ground in AI and Robotics! 🚀 Google DeepMind has unveiled an AI-powered table tennis robot that is taking the game to a whole new level! 🏓🤖 This innovative robot, equipped with advanced AI and a sophisticated robotic arm, has achieved amateur human-level performance in table tennis. It won 45% of its matches against human players, showcasing its impressive adaptability and quick decision-making skills12. What makes this development truly remarkable is the robot’s ability to learn and improve its gameplay in real-time, adapting to different opponents’ styles. This breakthrough not only highlights the potential of AI in mastering complex physical tasks but also opens up new possibilities for AI applications in various fields. Kudos to the brilliant minds at Google DeepMind for pushing the boundaries of what’s possible with AI and robotics! 🌟 #AI #Robotics #Innovation #TableTennis #GoogleDeepMind #TechNews

🚀 Groundbreaking Robotics sports by Google DeepMind ! 🤖🏓 🆕 Is it the beginning of a near future era where Olympic Games would include Robots Vs Humans as a Category? as stated by Khalid Almuraee 🏓 Deepmind has disrupted once again, making of Artificial Intelligence a solution to overpass human capabilities as already they did with the Go Game #AlphaGo. 🦄 This significant advancement in robotics with the development of the first learned robot agent capable of reaching amateur human-level performance in competitive Table Tennis! 🌟 💡Key Highlights: - 🏓 Human-Level Performance: The robot won 45% of matches against 29 human players with varying skill levels, and 100% for beginners. - 🧠 Advanced AI: Utilizing a hierarchical and modular policy architecture, the robot adapts in real-time to unseen opponents. - 🎯 Zero-Shot Sim-to-Real: Innovative techniques enabled the robot to seamlessly transition from simulation to the real world. - 🎥 Real-Time Matches: The robot was put to the test, competing against beginners, intermediates, and even advanced players. 💡 Why Table Tennis? the successor for Go including Robotics control. Table tennis is more than just a game—it’s a complex, high-speed sport that requires a unique combination of physical skill and strategic thinking. Unlike purely strategic games like Chess or Go, table tennis demands rapid, precise movements and in-the-moment decision-making, making it an ideal benchmark for advancing robotic capabilities. Key Contributions: 1. A cutting-edge hierarchical and modular policy architecture. 2. Techniques for zero-shot sim-to-real deployment. This will be disruptive for Local Digital Twin Toolbox as the deployment of Traffic models and Smart Cities models. #LDT 3. Real-time adaptation to previously unseen opponents. 4. A comprehensive user study where the robot played against human players in physical environments. Coach’s Perspective: _"The robot exceeded even my expectations. It was a true honor to be part of this research."_ - stated by Barney, Professional #TableTennis Coach 🎥 Watch the Matches | 📜 Read the Paper | 🎓 Explore Related Research in: https://lnkd.in/dYveNYBf Congratulations to David D'Ambrosio 👏 David Thacker Saminda Abeyruwan Laura Graesser Atil Iscen Heni Ben Amor Alex Bewley Barney J. Reed Krista R. Leila Takayama Yuval Tassa Krzysztof Choromanski Erwin Coumans Deepali Jain Navdeep Jaitly Natasha Jaques Satoshi Kataoka Yuheng Kuang Nevena Lazic Reza Mahjourian Sherry Moore Kenneth O. Anish Shankar Vikas Sindhwani Vincent Vanhoucke Grace Vesom Peng Xu Pannag Sanketi Google Google Research Robotics Robotic Process Automation Libelium Enrique Gomicia SCAI | سكاي Javier Solobera Bernardino Romera Paredes Table Tennis England #Robotics #AI #TableTennis #Innovation #SimToReal #MachineLearning #ReinforcementLearning #RoboticsResearch #HumanRobotInteraction

Google’s DeepMind, has developed a robotic arm that can play table tennis—and it’s not just a novelty. This robot has already taken on human opponents and won 13 out of 29 matches against amateur players. Not bad for a machine, right? What makes this so impressive? Well, this is the first time a robot has been trained to compete at a human level in a sport. The DeepMind team pulled this off by creating a "skill library" filled with basic actions like serving and rallying. They also used sim-to-sim adapters, relying on a detailed dataset of game states to help the robot learn. The training process was pretty intense. The robot learned to make real-time decisions by simulating ball movements in a virtual environment, all thanks to a method called Blackbox Gradient Sensing. But the real magic happens in real-time. The robot is equipped with 20 cameras that keep a close eye on the opponent’s paddle. This high-tech setup allows the robot to track the ball, follow the human player’s moves, and even adapt to the game’s flow. And it’s not just about the tech—this is a glimpse into the future. As robots and AI continue to evolve, we’re likely to see even more of these once-fictional scenarios becoming part of our everyday lives. Who knows? Maybe one day, we'll all be cheering for our favorite AI athletes alongside our human ones. Featured Image: Via Google DeepMind