用可重构被动关节实现低成本灵巧抓取，单样本学习即可自适应配置。
Underactuated dexterous robotic grasping with reconfigurable passive joints
- 通过外力重构无驱动的被动关节，仅靠腱张力锁定完成复杂操作。
- 在42个宜家物品上达80%成功率，YCB数据集上达87%。
- 支持从单例学习中自动配置关节，适合轻量级灵巧手应用。
我们提出一种新型可重构被动关节（RP-joint），并将其应用于欠驱动三指机械手。该关节无主动驱动，结构轻巧紧凑，可通过外部力轻松重构，并在腱张力作用下锁定，以执行复杂灵巧操作。同时，我们提出一种方法，使欠驱动机械手能从单个示例中学习灵巧抓取，并自动配置RP关节。该方法通过引入运动学接触优化进一步提升抓取性能。所提出的机械手与抓取规划器在42个宜家物体和YCB物体数据集上进行了测试，共执行超过370次抓取，分别取得80%和87%的抓取成功率。
原文摘要 · Abstract (English)
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.
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