抓取力度应多大？
How Firm Should a Grasp Be?
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中文总结 AI 辅助
本研究针对机器人抓取力度平衡问题，提出视觉-触觉实时估计物体材料属性的方法，构建含真实物体物理属性的数据集并验证，实现适配物体特性的轻柔抓取。
中文摘要 AI 辅助
理想的机器人抓取应足够牢固以稳定操控物体，同时足够轻柔以避免损坏物体。要实现这种平衡需要了解物体的材料属性，如质量、弹性和表面摩擦系数，但这些属性很少能先验地精确获知。本研究提出一种视觉-触觉方法，用于在抓取过程中实时估计材料属性，利用这些估计出的属性确定操控物体所需的最小抓取力。我们贡献了一个包含真实世界物体（水果和蔬菜）的新数据集，这些物体带有已测量的物理属性（形状、质量、弹性和摩擦系数），我们通过该数据集构建了力估计模型。我们使用带有平行爪式夹具的机器人对抓取力控制方法进行实验验证，证明了系统能够轻柔抓取多种物体，且每种情况下都能适配物体的独特物理属性。
英文摘要
An ideal robot grasp is firm enough to securely handle an object, yet gentle enough to avoid damaging it. Achieving this balance requires knowledge of the object's material properties, such as its mass, elasticity, and surface friction. These properties, however, are seldom precisely known a priori. In this work, we propose a visuotactile approach to estimating material properties in real time, during the process of grasping. Our method uses these estimated properties to determine the minimum grasp force required to handle the object. We contribute a new dataset of real-world objects (fruits and vegetables) with measured physical properties (shape, mass, elasticity, and friction), which we use to construct our force estimation model via simulations. We experimentally validate our approach to grasp force control using a robot with a parallel-jaw gripper. We demonstrate our system's ability to gently grasp a wide variety of objects, in each case adapting to their unique physical properties.
发表机构
- Columbia University（哥伦比亚大学）
机构由 AI 辅助整理，请以论文原文为准。
