# HiTac-WAM：用于富接触机器人操作的分层触觉世界动作模型

> 原文链接：https://robotics.hellobike.com/papers/2026-08-20-2608-19574
> 频道：papers | 更新日期：2026-08-20 | 标签：arxiv, cs.RO
> 核心摘要：针对现有触觉世界动作模型未分层建模物理依赖，HiTac-WAM分解预测触觉状态并用于规划，在三项真机操作中验证。

# HiTac-WAM：用于富接触机器人操作的分层触觉世界动作模型

::: v-pre
> 原文链接：[HiTac-WAM: A Hierarchical Tactile World Action Model for Contact-Rich Robot Manipulation](https://arxiv.org/abs/2608.19574)（arXiv 2608.19574 · 发布日期：2026-08-20）

**HiTac-WAM: A Hierarchical Tactile World Action Model for Contact-Rich Robot Manipulation**

针对现有触觉世界动作模型未分层建模物理依赖，HiTac-WAM分解预测触觉状态并用于规划，在三项真机操作中验证。

## 核心要点

- 现有触觉感知模型多将未来触觉表示为图像或潜变量，缺少对触觉层级物理依赖的建模。
- HiTac-WAM在执行前为候选动作块预测接触状态、3D形变场和滑移风险的层级触觉序列。
- 模型用定向注意力隔离触觉查询，并据触觉预测与任务进度排序候选动作、触发纠正性重规划。
- 实验中接触F1均值达0.921，并在三项真机任务中将平均成功率从31.1%提升至72.2%。

**验证范围与局限**：验证范围为芯片抓取、黑板擦除和USB插入三项真机操作任务。

## 英文摘要 (Abstract)

World action models jointly predict future visual observations and actions, whereas existing tactile-aware variants typically represent future touch as an image or latent stream without modeling the physical dependencies that organize tactile states hierarchically. We present HiTac-WAM, a hierarchical tactile world action model that forecasts a sequence of future tactile states for each candidate action chunk before execution. The forecast factorizes into contact state, a 3D deformation field, and slip risk, organized as a directed hierarchy in which each downstream stage is conditioned on stop-gradient signals from preceding stages. A directed attention mask allows tactile queries to attend to the video-action context of each candidate while preventing video and action queries from attending to tactile tokens. For planning, HiTac-WAM ranks candidate action chunks using tactile forecasts and task-progress estimates. For execution, the selected tactile forecast is retained as a reference; persistent discrepancies between predicted and observed tactile states trigger corrective replanning. HiTac-WAM achieves a mean contact F1 of 0.921; under matched training budgets, the directed hierarchy reduces 3D displacement L2 error by 17.6% relative to the deformation-only predictor and improves slip AUPRC by 60.4% relative to the slip-only predictor. Across chip grasping, blackboard erasing, and USB insertion, selection guided by the hierarchical forecasts increases the average real-robot success rate from 31.1% to 61.1%, while the full system attains 72.2%.

作者：Chao Xue, Chaofan Zhang, Wenxuan Ma, Guocai Yao, Shaowei Cui, Shuo Wang
:::

## 原始参考来源 (Sources)

- [arXiv 2608.19574：HiTac-WAM: A Hierarchical Tactile World Action Model for Contact-Rich Robot Manipulation](https://arxiv.org/abs/2608.19574)