用触觉传感器实时估计算软指抓握力与接触状态，无需特定物体训练。
Soft Finger Grasp Force and Contact State Estimation from Tactile Sensors
- 基于神经网络从软体传感器数据回归抓握力。
- 在多种物体上实现接触状态准确估计，支持任务级控制。
- 方法通用性强，无需针对特定物体重新训练。
软体机械手指在抓取和操作中能提升适应性，弥补物体或环境接触的几何差异，但目前仍缺乏足够的力控能力和精细操作能力。集成式触觉传感器可提供抓取与任务相关信息以增强灵巧性，理想情况下不应依赖特定物体的训练。手指所受的总力矢量可反映内部抓握力（如抓取稳定性），当多指合力相加时，可估算作用于物体上的外部力（如任务级控制）。本研究探讨了通过集成软体传感器估计手指力的有效性，并用于接触状态判断。采用神经网络进行力回归，使用力/力矩传感器采集标注数据，涵盖多种测试物体。随后在插拔任务场景中应用该模型，验证其在接触状态估计中的有效性。
原文摘要 · Abstract (English)
Soft robotic fingers can improve adaptability in grasping and manipulation, compensating for geometric variation in object or environmental contact, but today lack force capacity and fine dexterity. Integrated tactile sensors can provide grasp and task information which can improve dexterity,but should ideally not require object-specific training. The total force vector exerted by a finger provides general information to the internal grasp forces (e.g. for grasp stability) and, when summed over fingers, an estimate of the external force acting on the grasped object (e.g. for task-level control). In this study, we investigate the efficacy of estimating finger force from integrated soft sensors and use it to estimate contact states. We use a neural network for force regression, collecting labelled data with a force/torque sensor and a range of test objects. Subsequently, we apply this model in a plug-in task scenario and demonstrate its validity in estimating contact states.
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