Humanoid robot uses human data to master cartwheels and sprints

Researchers at Cornell University have developed BeyondMimic, a novel framework enabling humanoid robots to perform complex, fluid human-like motions such as cartwheels, sprints, dance moves, and even Cristiano Ronaldo’s “Siu” celebration. Unlike traditional programming methods that require task-specific coding, BeyondMimic uses human motion capture data to train robots through a unified policy, allowing them to generalize and execute new tasks without prior training. This system leverages Markov Decision Processes and hyperparameters to seamlessly transition between diverse movements while preserving the style, timing, and expression of the original human actions. A key innovation in BeyondMimic is the use of loss-guided diffusion, which guides the robot’s real-time movements via differentiable cost functions, ensuring accuracy, flexibility, balance, and stability. The framework supports various real-world robotic controls such as path following, joystick operation, and obstacle avoidance, making it highly adaptable. The entire training pipeline is open-source and reproducible, providing a