The human hand is a complex, highly-articulated system, which has been the source of inspiration in designing humanoid robotic and prosthetic hands. Understanding the functionality of the human hand is crucial for the design, efficient control and transfer of human versatility and dexterity to such anthropomorphic robotic hands. Although research in this area has made significant advances, the synthesis of grasp configurations, based on observed human grasping data, is still an unsolved and challenging task. In this work we derive a novel, constrained autoencoder model, that encodes human grasping data in a compact representation. This representation encodes both the grasp type in a three-dimensional latent space and the object size as an explicit parameter constraint allowing the direct synthesis of object-specific grasps. We train the model on 2250 grasps generated by 15 subjects using 35 diverse objects from the KIT and YCB object sets. In the evaluation we show that the synthesized grasp configurations are human-like and have a high probability of success under pose uncertainty.

中文翻译：

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拟人化手的特定对象抓握的受人类启发的表示

人手是一个复杂的、高度关节化的系统，它一直是设计人形机器人和假手的灵感来源。了解人手的功能对于设计、有效控制以及将人类的多功能性和灵巧性转移到这种拟人化的机器人手上至关重要。尽管该领域的研究取得了重大进展，但基于观察到的人类抓取数据合成抓取配置仍然是一项未解决且具有挑战性的任务。在这项工作中，我们推导出一种新颖的、受约束的自动编码器模型，该模型以紧凑的表示形式对人类抓取数据进行编码。这种表示将三维潜在空间中的抓取类型和对象大小编码为显式参数约束，允许直接合成特定对象的抓取。我们使用来自 KIT 和 YCB 对象集中的 35 个不同对象对 15 名受试者生成的 2250 次抓握训练模型。在评估中，我们表明合成的抓取配置类似于人类，并且在姿势不确定性下具有很高的成功概率。