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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

Chinese

实用的转移大人运动数据集已成为通用、表达式人体全身控制的主要路径,但跨多个动作的扩展是昂贵的。当前的管道使用模型免费的增强学习,要么通过损失蒸发将每运动专家合并,要么通过集群规模计算强迫单一政策。SuperMimic通过共享动态的单个可区分的世界模型减轻了所有动作的学习。从一个标准固体模拟器的日志转移到一个标准固体模拟器的移动转移,SuperMimic超越了世界模型和追踪政策列车在一个循环中,以及在7天内在128个GPU1上训练的背后扩展时间给出了短想象中的分析错误。快速想象的滚动和分析梯度使政策学习效率

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

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange