Contact-rich manipulation requires robots to continuously perceive and regulate evolving physical interactions under dynamic contact transitions or complex surface geometries. Recent imitation learning methods improve contact-aware control by incorporating tactile or force feedback, but they rarely model the asymmetric spatiotemporal roles of global force and local tactile sensing. To address this, we propose TacForeSight, a lightweight force-conditioned tactile foresight framework for real-time manipulation. The core component is TacForceWM, a tactile world model that predicts short-horizon tactile latent dynamics from dual-finger tactile observations conditioned on high-frequency wrist force and torque signals. Another key component, the Predictive Tactile-Conditioned Policy, leverages the predicted latents as anticipatory contact priors, models the current-to-future tactile evolution via cross-attention, and adaptively fuses visuo-tactile features through a tactile-guided gating module. By forecasting purely within a compact latent space, TacForeSight enables proactive contact reasoning with efficient real-time inference suitable for high-frequency manipulation control. Real-robot experiments on five representative tasks and three in-process perturbation settings show that TacForeSight consistently outperforms existing baselines, particularly under dynamic contact disturbances. All models and datasets will be made publicly available on the project website at https://tacforesight.github.io/ProjectPage.

翻译：密集接触操作要求机器人在动态接触过渡或复杂曲面几何结构下持续感知并调节演变的物理交互。近期模仿学习方法通过融合触觉或力反馈提升了接触感知控制能力，但鲜有研究对全局力与局部触觉感知的非对称时空作用进行建模。针对此问题，我们提出TacForeSight——一种面向实时操控的轻量化力条件触觉预测框架。其核心组件TacForceWM是一种触觉世界模型，能够以高频腕部力/力矩信号为条件，从双指触觉观测中预测短时域触觉隐式动力学。另一关键组件——预测式触觉条件策略——将预测的隐层特征作为前瞻性接触先验，通过交叉注意力建模当前到未来的触觉演化，并通过触觉引导门控模块自适应融合视觉-触觉特征。通过仅在紧凑隐空间中进行预测，TacForeSight可实现适用于高频操控控制的高效实时推理与主动接触推理。在五项代表性任务及三种进程扰动设置下的真实机器人实验中，TacForeSight一致性优于现有基准方法，尤其在动态接触干扰场景下表现突出。所有模型与数据集将发布于项目网站https://tacforesight.github.io/ProjectPage。