用深度学习+有限元分析，让触觉传感器精准感知压力与形变。
TensorTouch: Calibration of Tactile Sensors for High Resolution Stress Tensor and Deformation for Dexterous Manipulation
- 融合有限元分析与深度学习，从图像中提取像素级接触信息
- 实现亚毫米级定位精度和高精度力估计，支持大形变
- 适合需精细触觉的机器人操作任务，如抓取细绳
涉及多表面同时接触的复杂灵巧操作（如从地面夹起硬币或处理纠缠物体）对机器人系统仍是挑战。此类任务超越视觉与本体感知能力，需具备校准物理量的高分辨率触觉传感。原始光学触觉传感器图像虽信息丰富，但缺乏可解释性与跨传感器通用性，限制了实际应用。TensorTouch通过结合有限元分析与深度学习，从光学触觉传感器中提取全面接触信息，包括应力张量、形变场及像素级受力分布。该框架实现亚毫米级位置精度与精确的力估计，支持大变形，对操控柔软物体至关重要。实验验证其在基于检测到的运动选择性抓取两根细绳之一时达到90%成功率，实现了此前机器人难以企及的高接触密度操作能力。
原文摘要 · Abstract (English)
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.
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