IEEE Trans Haptics. 2023 Jul-Sep;16(3):400-411. doi: 10.1109/TOH.2023.3300439. Epub 2023 Sep 19.
Human-like robot hands provide the flexibility to manipulate a variety of objects that are found in unstructured environments. Knowledge of object properties and motion trajectory is required, but often not available in real-world manipulation tasks. Although it is possible to grasp and manipulate unknown objects, an uninformed grasp leads to inferior stability, accuracy, and repeatability of the manipulation. Therefore, a central challenge of in-hand manipulation in unstructured environments is to acquire this information safely and efficiently. We propose an in-hand manipulation framework that does not assume any prior information about the object and the motion, but instead extracts the object properties through a novel haptic exploration procedure and learns the motion from demonstration using dynamical movement primitives. We evaluate our approach by unknown object manipulation experiments using a human-like robot hand. The results show that haptic exploration improves the manipulation robustness and accuracy significantly, compared to the virtual spring framework baseline method that is widely used for grasping unknown objects.
类人机器人手提供了操纵各种在非结构化环境中发现的物体的灵活性。需要了解物体的属性和运动轨迹，但在实际操作任务中通常无法获得这些信息。虽然可以抓取和操纵未知物体，但不知情的抓取会导致操作的稳定性、准确性和可重复性降低。因此，非结构化环境中的在手操作的一个核心挑战是安全高效地获取这些信息。我们提出了一种在手操作框架，该框架不假设关于物体和运动的任何先验信息，而是通过新颖的触觉探索过程来提取物体属性，并使用动力学运动基元从演示中学习运动。我们使用类人机器人手进行了未知物体操作实验来评估我们的方法。结果表明，与广泛用于抓取未知物体的虚拟弹簧框架基线方法相比，触觉探索显著提高了操作的鲁棒性和准确性。
