arXiv cs.AI· Yusen Feng, Bingchen Han, Jiangran Lyu, Kai Liu, Yixin Zheng, Yuxuan Wan, Weiheng Liu, Sun Han, Ruiqin Li, Yulong Zhang, Fangfu Liu, Xuesong Shi, Libin Liu, Yizhou Wang, Zhizheng Zhang, He Wang·· 12 小时前AI 评分35
WAM-TTT：通过测试时观看人类游玩来引导世界动作模型
WAM-TTT: Steering World-Action Models by Watching Human Play at Test Time
AI 导读
WAM-TTT 是一个测试时训练框架，可从原始人类视频中引导世界动作模型（WAM），无需机器人动作、人工标注或任务特定微调。它通过自监督视频预测将人类视频吸收进冻结 WAM 内的轻量自适应记忆，并用配对人-机器人数据的键值记忆重建目标做元训练对齐。测试时仅需无标注人类视频即可适配记忆，在多种操作任务和泛化设置上持续优于上下文人类视频条件基线。
正文 · 原文
Authors:Yusen Feng, Bingchen Han, Jiangran Lyu, Kai Liu, Yixin Zheng, Yuxuan Wan, Weiheng Liu, Sun Han, Ruiqin Li, Yulong Zhang, Fangfu Liu, Xuesong Shi, Libin Liu, Yizhou Wang, Zhizheng Zhang, He Wang
Abstract:Steering robot foundation models (RFMs) toward new task variants or user-preferred behaviors remains challenging, often requiring additional robot demonstrations, task-specific fine-tuning, or long-context conditioning. We present WAM-TTT, a test-time training framework for steering world action models from raw human videos. Rather than treating human videos as trajectories to imitate, WAM-TTT absorbs them into a lightweight adaptive memory inside a frozen WAM through self-supervised video prediction. To make this memory useful for control, we introduce a meta-training stage that aligns human demonstrations with robot behaviors using paired human-robot data and a key--value memory reconstruction objective. At test time, only unlabeled human videos are required to adapt the memory, while the pretrained WAM remains frozen. This enables efficient and reusable steering without robot actions, human-side annotations, or task-specific fine-tuning, while preserving the generalization ability of the foundation model. Extensive experiments show that WAM-TTT consistently outperforms in-context human-video conditioning baselines across diverse manipulation tasks and generalization settings.
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.06988 [cs.RO] |
| (or arXiv:2607.06988v3 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2607.06988 arXiv-issued DOI via DataCite |
Submission history
From: Kai Liu [view email]
[v1]
Wed, 8 Jul 2026 04:23:22 UTC (10,111 KB)
[v2]
Fri, 10 Jul 2026 02:22:52 UTC (10,112 KB)
[v3]
Fri, 23 Oct 2026 06:57:20 UTC (10,585 KB)
来源：arXiv cs.AI · arxiv.org