欠驱动灵巧机器人抓取与可重构被动关节
Underactuated dexterous robotic grasping with reconfigurable passive joints
- Institute of Robotics and Machine Intelligence, Poznan University of Technology（波兹南理工大学机器人与智能机器研究所）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
提出一种新型可重构被动关节（RP-joint）及从单次示例学习灵巧抓取的方法，结合运动觉接触优化，在欠驱动三指夹持器上对IKEA和YCB物体分别实现80%和87%的抓取成功率。
中文摘要 AI 辅助
我们引入了一种新型可重构被动关节（RP-joint），该关节已在欠驱动三指机器人夹持器上实现并完成测试。RP-joint无驱动，但重量轻且结构紧凑。它可通过施加外力轻松重构，并锁定以执行复杂的灵巧操作任务，但前提是对连接的腱施加张力。此外，我们提出一种方法，能从欠驱动夹持器的单次示例中学习灵巧抓取，并自动配置RP-joint以进行灵巧操作。通过整合运动觉接触优化，进一步提升了抓取性能。所提出的RP-joint夹持器与抓取规划器在42个IKEA物体和YCB物体数据集上测试了超370次抓取，在IKEA和YCB上分别实现80%和87%的抓取成功率。
英文摘要
We introduce a novel reconfigurable passive joint (RP-joint), which has been implemented and tested on an underactuated three-finger robotic gripper. RP-joint has no actuation, but instead it is lightweight and compact. It can be easily reconfigured by applying external forces and locked to perform complex dexterous manipulation tasks, but only after tension is applied to the connected tendon. Additionally, we present an approach that allows learning dexterous grasps from single examples with underactuated grippers and automatically configures the RP-joints for dexterous manipulation. This is enhanced by integrating kinaesthetic contact optimization, which improves grasp performance even further. The proposed RP-joint gripper and grasp planner have been tested on over 370 grasps executed on 42 IKEA objects and on the YCB object dataset, achieving grasping success rates of 80% and 87%, on IKEA and YCB, respectively.
