LATENT enables the simulation of highly-dynamic tennis rallies by human athletes, showcasing versatile skills in a competitive environment. Developed by researchers from Tsinghua University, Peking University, and other institutions, it utilizes advanced AI techniques for real-time performance.

Noids, or neural boids, utilize a small neural network to generate steering forces based on visual input from each agent, comprising 1,922 learned parameters. This system mimics the behavior of real birds in a murmuration, where no leader or predetermined choreography directs the movement of the flock.

Robot dexterity is hindered by challenges related to high-ratio gearboxes, which negatively impact simulation-to-reality transfer and force transparency. These gearboxes are also prone to wear and tear, affecting overall robot performance.