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.

中文翻译：

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利用机器人手指尖触觉信息的柔软度自适应捏握策略


我们开发了一种基于触觉信息的捏握策略，使机器人手能够自适应地抓取容易变形的软物体。当机器人手必须执行抓取任务时，抓取规划器会根据视觉信息制定抓取策略。然而，仅使用视觉反馈无法正确检测目标对象的内在属性，例如柔软度。为了克服这一基本限制，我们的目标是使用指尖触觉信息开发一种柔软度自适应捏握策略。为了实现这一点，我们首先根据抗变形的特性对软物体进行分类。此外，我们设计了一种三维触觉传感器，通过测量和定位指尖上感应的分布力来提供触觉信息。在设计自适应抓取策略时，我们重点开发一种算法，使机器人手能够通过基于触觉反馈控制捏力来最小化物体变形来抓取柔软的物体。实验结果表明，通过所提出的策略，物体变形最多可减少约 83.5%。