Learning by Demonstration and Robust Control of Dexterous In-Hand Robotic Manipulation Skills
Learning by Demonstration and Robust Control of Dexterous In-Hand Robotic Manipulation Skills
复制标题
通过演示学习和鲁棒控制灵巧的手动机器人操作技能
DOI：
10.1109/iros40897.2019.8967567
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
L. Jamone
中科院分区：
文献类型：
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作者：
Gokhan Solak;L. Jamone
灵巧的机器人操纵未知物体可以为机器人在半结构化和非结构化环境中的新任务和应用开辟道路，从先进的工业制造到恶劣环境的探索。然而，它是具有挑战性的，至少有三个原因：所需的运动的对象可能是太复杂的分析描述，被操纵的对象的精确模型是不可用的，控制器应同时确保一个强大的把握和有效的手的运动。为了解决这些问题，我们建议从人类演示中学习手机器人操作任务，使用动态运动原语（DMPs），并复制它们与一个强大的兼容控制器的基础上的虚拟弹簧框架（VSF），采用实时反馈的接触力测量的机器人指尖。有了这个解决方案，DMPs的泛化能力可以成功地转移到灵巧的手操作问题：我们证明了这一点，提出了现实世界的实验中的手的平移和旋转的未知对象。
Dexterous robotic manipulation of unknown objects can open the way to novel tasks and applications of robots in semi-structured and unstructured settings, from advanced industrial manufacturing to exploration of harsh environments. However, it is challenging for at least three reasons: the desired motion of the object might be too complex to be described analytically, precise models of the manipulated objects are not available, the controller should simultaneously ensure both a robust grasp and an effective in-hand motion. To solve these issues we propose to learn in-hand robotic manipulation tasks from human demonstrations, using Dynamical Movement Primitives (DMPs), and to reproduce them with a robust compliant controller based on the Virtual Springs Framework (VSF), that employs real-time feedback of the contact forces measured on the robot fingertips. With this solution, the generalization capabilities of DMPs can be transferred successfully to the dexterous in-hand manipulation problem: we demonstrate this by presenting real-world experiments of in-hand translation and rotation of unknown objects.