Softness-Adaptive Pinch-Grasp Strategy Using Fingertip Tactile Information of Robot Hand

We develop a tactile information-based pinch-grasp strategy enabling a robot hand to adaptively grasp easily deformable soft objects. When a robot hand has to perform grasping tasks, the grasp planner develops the grasping strategy based on visual information. However, the intrinsic properties of the target object, such as softness, cannot be detected appropriately using only visual feedback. To overcome this fundamental limitation, we aim to develop a softness-adaptive pinch-grasp strategy using fingertip tactile information. To achieve this, we first categorize soft objects based on the characteristic of resistance to deformation. Moreover, we design a three-dimensional tactile sensor that provides tactile information by measuring and localizing the distributed forces induced on its fingertip. In devising the adaptive grasp strategy, we focus on developing an algorithm that enables a robot hand to grasp soft objects by minimizing object deformation by controlling the pinch force based on the tactile feedback. The experimental results demonstrate that object deformation can be reduced by up to approximately 83.5% by the proposed strategy.

Automated summary

The study developed a tactile information-based pinch-grasp strategy for a robot hand to adaptively grasp soft objects, utilizing categorization of soft objects based on resistance to deformation and the design of a 3D tactile sensor. With an algorithm focusing on minimizing object deformation through pinch force control based on tactile feedback, the proposed strategy successfully reduced object deformation by approximately 83.5% in experiments.