TacForeSight：面向接触丰富操作的力引导触觉世界模型
TacForeSight: Force-Guided Tactile World Model for Contact-Rich Manipulation
AI总结：
提出TacForeSight框架，通过力条件触觉世界模型预测触觉潜动态，结合预测性触觉条件策略实现高频操作下的主动接触推理，在动态接触干扰下优于现有方法。
摘要：
接触丰富操作要求机器人在动态接触过渡或复杂表面几何下持续感知和调节演变的物理交互。最近的模仿学习方法通过整合触觉或力反馈改善了接触感知控制，但很少对全局力和局部触觉感知的非对称时空角色进行建模。为此，我们提出TacForeSight，一种轻量级的力条件触觉预测框架，用于实时操作。核心组件是TacForceWM，一个触觉世界模型，它从双指触觉观测中预测短时域触觉潜动态，并以高频腕部力和力矩信号为条件。另一个关键组件，预测性触觉条件策略，利用预测的潜变量作为预期接触先验，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。通过在紧凑潜空间内进行预测，TacForeSight实现了主动接触推理，并具有适用于高频操作控制的高效实时推理。在五个代表性任务和三种过程扰动设置上的真实机器人实验表明，TacForeSight在动态接触干扰下始终优于现有基线。所有模型和数据集将在项目网站上公开。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
