Automating the act of grasping is one of the most compelling challenges in robotics. In recent times, a major trend has gained the attention of the robotic grasping community: soft manipulation. Along with the design of intrinsically soft robotic hands, it is important to devise grasp planning strategies that can take into account the hand characteristics, but are general enough to be applied to different robotic systems. In this article, we investigate how to perform top grasps with soft hands according to a model-based approach, using both power and precision grasps. The so-called closure signature (CS) is used to model closure motions of soft hands by associating to them a preferred grasping direction. This direction can be aligned to a suitable direction over the object to achieve successful top grasps. The CS-alignment is here combined with a recently developed AI-driven grasp planner for rigid grippers that is adjusted and used to retrieve an estimate of the optimal grasp to be performed on the object. The resulting grasp planner is tested with multiple experimental trials with two different robotic hands. A wide set of objects with different shapes was grasped successfully.

中文翻译：

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用软机器人手规划顶部抓握的手部闭合模型

自动化抓取行为是机器人技术中最引人注目的挑战之一。最近，一个主要趋势引起了机器人抓取社区的关注：软操作。随着本质上柔软的机器人手的设计，重要的是设计可以考虑手部特征的抓取规划策略，但足够通用以应用于不同的机器人系统。在本文中，我们研究如何根据基于模型的方法，同时使用有力和精确的抓握，用软手进行顶部抓握。所谓的闭合签名 (CS) 用于通过将软手的首选抓握方向与它们相关联来模拟软手的闭合运动。该方向可以与物体上方的合适方向对齐，以实现成功的顶部​​抓取。CS 对齐在这里与最近开发的用于刚性抓手的 AI 驱动抓握规划器相结合，该规划器经过调整并用于检索对对象执行的最佳抓握的估计。由此产生的抓取规划器通过两个不同的机械手进行了多次实验测试。成功抓取了大量不同形状的物体。