Object grasping is an important ability required for various robot tasks. In particular, tasks that require precise force adjustments during operation, such as grasping an unknown object or using a grasped tool, are difficult for humans to program in advance. Recently, AI-based algorithms that can imitate human force skills have been actively explored as a solution. In particular, bilateral control-based imitation learning achieves human-level motion speeds with environmental adaptability, only requiring human demonstration and without programming. However, owing to hardware limitations, its grasping performance remains limited, and tasks that involves grasping various objects are yet to be achieved. Here, we developed a cross-structure hand to grasp various objects. We experimentally demonstrated that the integration of bilateral control-based imitation learning and the cross-structure hand is effective for grasping various objects and harnessing tools.

翻译：物体抓取是各类机器人任务所需的重要能力。特别是需要在操作过程中精确调整力的任务，例如抓取未知物体或使用抓取的工具，对人类而言很难预先编程。近期，能够模仿人类力技能的基于人工智能的算法作为解决方案得到积极探索。其中，基于双手控制的模仿学习无需编程，仅需人类示范即可实现具备环境适应能力的人类级运动速度。然而，由于硬件限制，其抓取性能仍然有限，涉及抓取多种物体的任务尚未实现。为此，我们开发了一种能够抓取多种物体的交叉结构手爪。实验证明，将基于双手控制的模仿学习与交叉结构手爪相结合，对于抓取多种物体及使用工具是有效的。