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SuperMimic: Scaling Humanoid Whole-Body Control with One Differentiable World Model

Qiayuan Liao, Yiyang Shao, Junfeng Long, Kevin Zakka, Roberto Horowitz, Pieter Abbeel, Koushil Sreenath · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28480 · cited by 0 works (OpenAlex) · open-access PDF

French

Le suivi pratique de grands ensembles de données de transfert de mouvement humain est devenu le chemin dominant vers le contrôle de corps entier humain général et expressif, mais l'échelle à travers de nombreux mouvements est coûteuse. Les pipelines courantes utilisent l'apprentissage du renforcement sans modèle, soit en fusionnant les spécialistes du mouvement par distillation à perte, soit en forçant brutement une seule politique avec des calculs à échelle de cluster. SuperMimic amortit l'apprentissage à travers tous les mouvements avec un modèle mondial différenciable de leur dynamique partagée, adapté aux transitions enregistrées d'un simulateur de corps rigide standard. Le modèle mondial et le train de politique de suivi se réunissent dans une seule boucle, et la propagation

Machine translation (Meta M2M100); the English original follows.

Abstract

Tracking large human-motion datasets has become the dominant route to general, expressive humanoid whole-body control, but scaling across many motions is expensive. Current pipelines use model-free reinforcement learning, either merging per-motion specialists through lossy distillation, or brute-forcing a single policy with cluster-scale compute. SuperMimic amortizes learning across all motions with one differentiable world model of their shared dynamics, fit from logged transitions of a standard rigid-body simulator. The world model and the tracking policy train together in one loop, and backpropagation-through-time over short imagined rollouts gives the policy analytic gradients. Fast imagined rollouts and analytic gradients make policy learning efficient. In 3 hours on a single GPU, SuperMimic surpasses the return of a state-of-the-art motion tracker baseline trained for 7 days on 128 GPUs. It reduces GPU-hours by more than $1000\times$ and fails on only 25 of 129,514 benchmark motions, versus 179 failures for the baseline. After 9.8 hours on two GPUs, SuperMimic achieves zero failures. From 1 to 264 hours of motion, the world model's prediction error stays stable, and GPU-hours to a target return grow slowly. Our policy transfers to a physical Unitree G1 across diverse and acrobatic motions. SuperMimic removes compute as the bottleneck to scaling humanoid whole-body control, making it practical to measure motion-tracking scaling laws.

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Topics: Reinforcement learning, Computer science, Scaling, Bottleneck, Benchmark (surveying), Lossy compression

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