Robotic palpation relies on force sensing, but force signals in soft-tissue environments are variable and cannot reliably reveal subtle subsurface features. We present a compact multimodal sensor that integrates high-resolution vision-based tactile imaging with a 6-axis force-torque sensor. In experiments on silicone phantoms with diverse subsurface tendon geometries, force signals alone frequently produce ambiguous responses, while tactile images reveal clear structural differences in presence, diameter, depth, crossings, and multiplicity. Yet accurate force tracking remains essential for maintaining safe, consistent contact during physiotherapeutic interaction. Preliminary results show that combining tactile and force modalities enables robust subsurface feature detection and controlled robotic palpation.

翻译：机器人触诊通常依赖于力觉传感，但在软组织环境中，力信号具有多变性，无法可靠地揭示细微的皮下特征。我们提出了一种紧凑型多模态传感器，它将基于视觉的高分辨率触觉成像与六轴力-力矩传感器相结合。在对具有多种皮下肌腱几何形状的硅胶模型进行的实验中，仅凭力信号常常产生模糊的响应，而触觉图像则清晰地揭示了在存在性、直径、深度、交叉和多重性方面的结构差异。然而，精确的力跟踪对于在物理治疗交互过程中维持安全、一致的接触仍然至关重要。初步结果表明，结合触觉和力觉模态能够实现稳健的皮下特征检测和受控的机器人触诊。