# WholeBodyWAM：利用可扩展运动先验学习全身世界动作模型

> 原文链接：https://robotics.hellobike.com/papers/2026-09-16-2609-18197
> 频道：papers | 更新日期：2026-09-16 | 标签：arxiv, cs.RO
> 核心摘要：WholeBodyWAM利用异构大规模运动预训练全身运动先验，并在真实世界类人操作中验证可提升预测、任务表现和数据效率。

# WholeBodyWAM：利用可扩展运动先验学习全身世界动作模型

::: v-pre
> 原文链接：[WholeBodyWAM: Learning Whole-Body World Action Models with Scalable Motion Priors](https://arxiv.org/abs/2609.18197)（arXiv 2609.18197 · 发布日期：2026-09-16）

**WholeBodyWAM: Learning Whole-Body World Action Models with Scalable Motion Priors**

WholeBodyWAM利用异构大规模运动预训练全身运动先验，并在真实世界类人操作中验证可提升预测、任务表现和数据效率。

## 核心要点

- 类人全身操作需要协调动力学，但目标机器人轨迹昂贵且难以规模化采集。
- 整理UniMotion-4K，将人类视频、3D运动和异构类人平台统一到运动空间。
- 语言条件Motion Expert预训练预测未来全身运动，并经非对称MoT融合视频和动作专家。
- 实验显示随运动预训练规模增大持续受益，并提升预测、任务表现和少示范数据效率。

**验证范围与局限**：在真实世界类人机器人操作中验证，摘要未说明具体任务或机器人数量。

## 英文摘要 (Abstract)

Humanoid whole-body manipulation requires coordinated whole-body dynamics, yet large-scale trajectories from a target robot are expensive to collect and difficult to scale. In contrast, whole-body motion from human and humanoid sources is abundantly available, although such data cannot be directly used as embodiment-specific robot actions. This work asks whether these scalable motion resources can instead provide a transferable predictive prior for humanoid world-action modeling. We introduce WholeBodyWAM, a humanoid world-action model that learns whole-body dynamics from large-scale heterogeneous motion before target-robot training. We curate UniMotion-4K, a motion corpus spanning more than 4K hours from human videos, native 3D motion datasets, and heterogeneous humanoid platforms, and canonicalize these diverse sources into a unified motion space. A language-conditioned Motion Expert is then pretrained to predict future whole-body motion without target-robot action supervision. During robot post-training, the pretrained Motion Expert is integrated with Video and Action Experts through asymmetric Mixture-of-Transformers (MoT) attention, enabling predictive scene dynamics and whole-body motion to jointly inform embodiment-specific action generation. Experiments show that WholeBodyWAM consistently benefits from increased motion-pretraining scale, improves future-motion prediction and downstream task performance, and transfers effectively to real-world humanoid manipulation. Moreover, the pretrained motion prior substantially improves data efficiency under limited target-robot demonstrations.

作者：Bowei Zhang, Qiyao Zhang, Shuanghao Bai, Xinhua Wang, Meng Li, Yilei Wang, Leiwang Zhang, Jian Tang, Lu Zhou, Lei Sun
:::

## 原始参考来源 (Sources)

- [arXiv 2609.18197：WholeBodyWAM: Learning Whole-Body World Action Models with Scalable Motion Priors](https://arxiv.org/abs/2609.18197)