Training

Training gives humanoid robots the ability to perceive, plan, and act. It turns recorded data — from sensors, demonstrations, or simulations — into working models that guide real-world behavior.

Training methods vary by architecture. Imitation learning uses expert demonstrations to copy behaviors. Reinforcement learning explores through trial and error, optimizing reward-based performance. End-to-end approaches, especially those using foundation models, consume vast multimodal datasets — video, language, 3D scans — to generalize across tasks.

Modern humanoids blend multiple training sources: real-world logs, simulation rollouts, synthetic scenes, and teleoperated sessions. The goal is data efficiency: learning more from less, with fewer collisions, faster convergence, and better generalization.

Training doesn’t end when a robot ships. Logs from deployment become new supervision. Robots improve not just in labs, but in the field — tightening the feedback loop between experience and intelligence.