VioLA: Learning Generalist Humanoid Control Policies from Human Data
VioLA introduces a generalist humanoid policy that learns to predict body and hand motion latents instead of direct joint commands. By mapping human and robot motions into a shared latent space, the policy can perform zero-shot locomotion and manipulation on real robots without needing task-specific fine-tuning. This method leverages human demonstrations to train a single model capable of handling diverse tasks.