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.

翻译：软体机器人手指能够提升抓取与操作中的适应性，补偿物体或环境接触中的几何变化，但当前仍缺乏力量容量与精细灵巧性。集成的触觉传感器可提供抓取与任务信息以增强灵巧性，但理想情况下不应需要针对特定物体的训练。手指施加的总力向量为内部抓取力（例如用于抓取稳定性）提供通用信息，并在各手指间求和时，可估计作用于被抓取物体的外力（例如用于任务级控制）。在本研究中，我们探究了从集成软体传感器估计手指力的效能，并利用其估计接触状态。我们采用神经网络进行力回归，通过力/力矩传感器与一系列测试物体收集标注数据。随后，我们将该模型应用于插件任务场景，并证明其在估计接触状态方面的有效性。