TensorTouch：面向灵巧操作的高分辨率应力张量与形变触觉传感器校准
TensorTouch: Calibration of Tactile Sensors for High Resolution Stress Tensor and Deformation for Dexterous Manipulation
AI总结：
本文提出TensorTouch框架，融合有限元分析与深度学习校准光学触觉传感器，以像素级输出应力张量、形变场和力分布，实现亚毫米定位与精确力估计，并在绳线选择性抓取中达到90%成功率。
摘要：
涉及在不同表面上同时发生多处接触的高级灵巧操作，例如从地面夹取硬币或操作相互缠绕的物体，对机器人系统而言仍具挑战性。此类任务超出了仅凭视觉和本体感觉所能实现的能力范围，需要具备经校准物理量度的高分辨率触觉感知。原始光学触觉传感器图像虽然信息丰富，但缺乏可解释性和跨传感器迁移能力，从而限制了其在现实世界中的实用性。TensorTouch通过将有限元分析与深度学习相结合来应对这一挑战，从光学触觉传感器中提取全面的接触信息，包括像素级分辨率的应力张量、形变场和力分布。TensorTouch框架实现了亚毫米级位置精度和精确的力估计，同时支持对操作柔软物体至关重要的大传感器形变。实验验证表明，系统能够根据检测到的运动，在两根绳线中有选择地抓取其中一根，成功率达到90%，从而实现了机器人系统此前无法获得的新型富接触操作能力。
英文摘要：
Advanced dexterous manipulation involving multiple simultaneous contacts across different surfaces, like pinching coins from ground or manipulating intertwined objects, remains challenging for robotic systems. Such tasks exceed the capabilities of vision and proprioception alone, requiring high-resolution tactile sensing with calibrated physical metrics. Raw optical tactile sensor images, while information-rich, lack interpretability and cross-sensor transferability, limiting their real-world utility. TensorTouch addresses this challenge by integrating finite element analysis with deep learning to extract comprehensive contact information from optical tactile sensors, including stress tensors, deformation fields, and force distributions at pixel-level resolution. The TensorTouch framework achieves sub-millimeter position accuracy and precise force estimation while supporting large sensor deformations crucial for manipulating soft objects. Experimental validation demonstrates 90% success in selectively grasping one of two strings based on detected motion, enabling new contact-rich manipulation capabilities previously inaccessible to robotic systems.
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。
