Tendon-based underactuated hands are intended to be simple, compliant and affordable. Often, they are 3D printed and do not include tactile sensors. Hence, performing in-hand object recognition with direct touch sensing is not feasible. Adding tactile sensors can complicate the hardware and introduce extra costs to the robotic hand. Also, the common approach of visual perception may not be available due to occlusions. In this paper, we explore whether kinesthetic haptics can provide in-direct information regarding the geometry of a grasped object during in-hand manipulation with an underactuated hand. By solely sensing actuator positions and torques over a period of time during motion, we show that a classifier can recognize an object from a set of trained ones with a high success rate of almost 95%. In addition, the implementation of a real-time majority vote during manipulation further improves recognition. Additionally, a trained classifier is also shown to be successful in distinguishing between shape categories rather than just specific objects.

翻译：腱驱动欠驱动手的设计目标为简单、柔顺且成本低廉。这类手通常采用3D打印制造，不包含触觉传感器。因此，通过直接触觉感知进行手内物体识别并不现实。加装触觉传感器会使机械手硬件复杂化并增加额外成本。同时，常见的视觉感知方法也可能因遮挡而无法使用。本文探究了动觉触觉能否在欠驱动手进行手内操作时，间接提供抓取物体几何形状的信息。通过仅感知运动过程中一段时间内的执行器位置与力矩，我们证明分类器能够以接近95%的高成功率从已训练物体集合中识别目标物体。此外，在操作过程中实施实时多数投票机制可进一步提升识别效果。实验还表明，训练后的分类器不仅能识别特定物体，还能成功区分不同形状类别。