TouchWorld：一种用于灵巧操作的预测性和反应性触觉基础模型
TouchWorld: A Predictive and Reactive Tactile Foundation Model for Dexterous Manipulation
AI总结：
研究针对日常环境中灵巧操作需预测与反应的问题，提出TouchWorld模型。该模型采用分层策略，分离多项任务。通过将触摸用于预测和反馈，提升局部接触适应性。在六个相关任务中表现出色，超越最强基线。
摘要：
在日常环境中的灵巧操作既需要预测也需要反应：机器人必须预测接触如何演变，同时快速纠正由滑动、未对准、不稳定抓握或力不匹配引起的局部误差。视觉和语言提供语义和几何指导，但无法可靠揭示隐藏的接触状态。虽然触觉传感能暴露这些物理线索，但现有大多数策略将触摸视为单一动作模型中的低频观测流。我们引入TouchWorld，一种用于灵巧操作的预测性和反应性触觉基础模型。它使用分层策略，包括视觉 - 语言子任务规划、触觉世界模型预测、视觉 - 触觉目标条件动作生成和高频触觉残差细化。通过将触摸用作预测性接触参考和快速反馈信号，TouchWorld在保持视觉 - 语言 - 动作策略语义泛化的同时改善了局部接触适应性。在六个长期且接触丰富的灵巧操作任务中，TouchWorld在干净设置下成功率达65.0%，在人为干扰下为53.7%，分别比最强基线高出15.7和18.5个百分点。
英文摘要：
Dexterous manipulation in everyday environments requires both anticipation and reaction: a robot must predict how contact should evolve while rapidly correcting local errors caused by slip, misalignment, unstable grasping, or force mismatch. Vision and language provide semantic and geometric guidance, but they cannot reliably reveal hidden contact states such as force, slip, and contact stability. Although tactile sensing exposes these physical cues, most existing policies treat touch as a low-frequency observation stream within a monolithic action model, coupling slow task reasoning, action generation, and fast contact feedback in a single loop. We introduce TouchWorld, a predictive-and-reactive tactile foundation model for dexterous manipulation. TouchWorld uses a hierarchical policy that separates vision-language subtask planning, tactile world-model prediction, visuo-tactile goal-conditioned action generation, and high-frequency tactile residual refinement. A High-Level Planning Layer produces executable subtasks and predicts tactile subgoals; a Visuo-Tactile Goal-Conditioned Policy generates nominal action chunks; and a Tactile-Conditioned Refinement Policy performs online residual correction using recent tactile and proprioceptive feedback. By using touch as both a predictive contact reference and a fast feedback signal, TouchWorld preserves the semantic generalization of vision-language-action policies while improving local contact adaptation. Across six long-horizon and contact-rich dexterous manipulation tasks, TouchWorld achieves 65.0% success in the clean setting and 53.7% success under human perturbations, outperforming the strongest baseline by 15.7 and 18.5 percentage points, respectively.
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。
