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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

Japanese

現行パイプラインは、モデルフリーの強化学習を使用し、損失の蒸発を通じて大規模なヒューマンモーションデータセットを統合するか、クラスタースケールコンピュータで単一のポリシーを強制するか、一般的で表現的なヒューマノイド全身コントロールへの主導的なルートとなっていますが、多くの動きをスケールすることは高価です。スーパーミミミックは、標準の固体体シミュレータのログされた動きの移行から適合して、共有ダイナミクスの1つの異なる世界モデルで、すべての動きの学習を縮小します。世界モデルと追跡ポリシー列車を1つのループで統合し、短いスケールコンピュータで単一の

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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