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4h ago

Humanoid Robot Learns Tennis From Imperfect Human Motion, Posts High Real-World Success

The open-source LATENT method builds a correctable action space from brief motion-capture fragments to transfer robust policies from simulation to the court.

The system enables the robot to sustain multi-shot rallies with human players.
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The remarkable Unitree G1 continues to astound with its abilities - now it's learned to play tennis

Overview

  • Researchers in China released a preprint and project code describing LATENT and showed real-world rallies on a Unitree G1 humanoid.
  • Roughly five hours of amateur motion-capture fragments seeded a latent action space that a high-level policy refined with reinforcement learning.
  • Evaluations across 10,000 trials reported up to 96.5% success in target-return tests and sustained multi-shot rallies against human players.
  • Independent reporting observed about 90% success on forehands and just under 80% on backhands during real-world demonstrations.
  • The team acknowledges reliance on motion capture and a simplified return-to-target task, proposing active vision and multi-agent training as next steps.