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✌️ Italy Ala 🇮🇹🦅

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Wednesday 26 August 2026 19:47:27 GMT

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2026-08-29 11:27:12

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2026-08-28 23:12:54

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2026-08-28 14:33:33

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2026-08-28 09:58:20

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2026-08-27 18:38:19

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2026-08-26 19:50:04

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A Unitree G1 humanoid robot is now playing TENNIS against a human. 🎾🤖 The robot in this tennis experiment is a Unitree G1 running LATENT, a research system developed for athletic humanoid tennis. LATENT stands for “Learning Athletic Humanoid Tennis Skills from Imperfect Human Motion Data.” The work is linked to Tsinghua University and Galbot and was deployed on the Unitree G1. What makes the demo interesting is the combination of perception, whole-body motion and timing required to return a tennis ball. The robot has to react to an incoming shot, position its body, coordinate its legs and upper body, control the racket trajectory, make contact with the ball and recover quickly enough for the next exchange. A tennis rally compresses many difficult robotics problems into a few seconds. LATENT was designed around imperfect human motion data rather than requiring perfectly captured professional tennis sequences. The researchers use fragments of human tennis motion as priors, then learn and compose the behaviors needed for the humanoid robot to strike incoming balls and return them toward target areas. The project also focuses on sim-to-real transfer. A movement that works in simulation can fail on hardware because of joint limits, actuator dynamics, latency, balance errors and differences between the simulated and physical environment. That makes a sustained rally much more useful than a single successful hit. For a humanoid robot, tennis tests several capabilities at once: • whole-body control • dynamic balance • reactive footwork • racket and arm coordination • fast motion planning • timing under uncertainty • recovery between movements • sim-to-real reinforcement learning Unitree’s G1 is a compact humanoid platform with 23 to 43 joint motors depending on configuration. Unitree positions it for imitation learning, reinforcement learning and embodied AI research. The bigger story is not whether a robot can beat a professional tennis player today. It cannot. What matters is that athletic tasks push humanoid control systems into conditions where mistakes become obvious. In tennis, a robot cannot simply replay one memorized trajectory. The ball arrives from different positions, timing changes from shot to shot, and the robot has to keep adjusting its body. That is exactly the kind of generalization embodied AI still needs. Sports are becoming a serious benchmark for humanoid robots. Running exposes locomotion limits. Football tests navigation, contact and coordination. Tennis adds fast visual feedback, prediction and precise whole-body timing. The LATENT researchers report that their policy can strike incoming balls across a range of conditions and sustain multi-shot rallies with human players in the real world. The project also released an open-source learning pipeline using MuJoCo for simulation, with tools for motion tracking, distillation and high-level policy learning. Training methods, motion datasets, reinforcement learning, perception and control software will determine what these machines can actually do outside a demo. Unitree G1 playing tennis is a strong example of Physical AI becoming visible. A humanoid robot is no longer being tested only on whether it can stand, walk or follow a fixed sequence. It is being pushed into a dynamic interaction with a human where every incoming ball creates a new control problem. Would you step onto the court and play a full match against a Unitree G1? @techniahqrobot Unitree G1 | Unitree Robotics | Humanoid Robot | Humanoid Tennis | Robot Tennis | LATENT | Galbot | Tsinghua University | Physical AI | Embodied AI | Reinforcement Learning | Sim-to-Real | Whole-Body Control | Robotics Research | AI Robotics #Unitree #UnitreeG1 #HumanoidRobot #PhysicalAI #EmbodiedAI

A Unitree G1 humanoid robot is now playing TENNIS against a human. 🎾🤖 The robot in this tennis experiment is a Unitree G1 running LATENT, a research system developed for athletic humanoid tennis. LATENT stands for “Learning Athletic Humanoid Tennis Skills from Imperfect Human Motion Data.” The work is linked to Tsinghua University and Galbot and was deployed on the Unitree G1. What makes the demo interesting is the combination of perception, whole-body motion and timing required to return a tennis ball. The robot has to react to an incoming shot, position its body, coordinate its legs and upper body, control the racket trajectory, make contact with the ball and recover quickly enough for the next exchange. A tennis rally compresses many difficult robotics problems into a few seconds. LATENT was designed around imperfect human motion data rather than requiring perfectly captured professional tennis sequences. The researchers use fragments of human tennis motion as priors, then learn and compose the behaviors needed for the humanoid robot to strike incoming balls and return them toward target areas. The project also focuses on sim-to-real transfer. A movement that works in simulation can fail on hardware because of joint limits, actuator dynamics, latency, balance errors and differences between the simulated and physical environment. That makes a sustained rally much more useful than a single successful hit. For a humanoid robot, tennis tests several capabilities at once: • whole-body control • dynamic balance • reactive footwork • racket and arm coordination • fast motion planning • timing under uncertainty • recovery between movements • sim-to-real reinforcement learning Unitree’s G1 is a compact humanoid platform with 23 to 43 joint motors depending on configuration. Unitree positions it for imitation learning, reinforcement learning and embodied AI research. The bigger story is not whether a robot can beat a professional tennis player today. It cannot. What matters is that athletic tasks push humanoid control systems into conditions where mistakes become obvious. In tennis, a robot cannot simply replay one memorized trajectory. The ball arrives from different positions, timing changes from shot to shot, and the robot has to keep adjusting its body. That is exactly the kind of generalization embodied AI still needs. Sports are becoming a serious benchmark for humanoid robots. Running exposes locomotion limits. Football tests navigation, contact and coordination. Tennis adds fast visual feedback, prediction and precise whole-body timing. The LATENT researchers report that their policy can strike incoming balls across a range of conditions and sustain multi-shot rallies with human players in the real world. The project also released an open-source learning pipeline using MuJoCo for simulation, with tools for motion tracking, distillation and high-level policy learning. Training methods, motion datasets, reinforcement learning, perception and control software will determine what these machines can actually do outside a demo. Unitree G1 playing tennis is a strong example of Physical AI becoming visible. A humanoid robot is no longer being tested only on whether it can stand, walk or follow a fixed sequence. It is being pushed into a dynamic interaction with a human where every incoming ball creates a new control problem. Would you step onto the court and play a full match against a Unitree G1? @techniahqrobot Unitree G1 | Unitree Robotics | Humanoid Robot | Humanoid Tennis | Robot Tennis | LATENT | Galbot | Tsinghua University | Physical AI | Embodied AI | Reinforcement Learning | Sim-to-Real | Whole-Body Control | Robotics Research | AI Robotics #Unitree #UnitreeG1 #HumanoidRobot #PhysicalAI #EmbodiedAI

#TIKTOK #CAPCUT #FORYOU #VIRAL #FYP

#TIKTOK #CAPCUT #FORYOU #VIRAL #FYP

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