A new system enables humanoid robots to play competitive tennis rallies with humans by learning from imperfect, fragmented motion data.
arXiv · March 16, 2026 · 2603.12686
Why it matters
This research overcomes the 'perfect data' bottleneck in humanoid robotics, demonstrating that a Unitree G1 can perform highly dynamic, target-oriented strikes and sustain rallies. It proves that complex athletic skills can be synthesized from motion fragments rather than requiring complete match sequences.
From the abstract
Human athletes demonstrate versatile and highly-dynamic tennis skills to successfully conduct competitive rallies with a high-speed tennis ball. However, reproducing such behaviors on humanoid robots is difficult, partially due to the lack of perfect humanoid action data or human kinematic motion data in tennis scenarios as reference. In this work, we propose LATENT, a system that Learns Athletic humanoid TEnnis skills from imperfect human motioN daTa. The imperfect human motion data consist onl