以数据手套为媒介的人手-机械手抓握技能传递OA

Imitation learning provides an effective way for transferring manipulation skills from human hands to ro-botic hands.However,traditional demonstration methods face problems including non-intuitive demonstration,poor reusability of demonstration data,and difficulties in effectively transferring tactile and kinesthetic perception fea-tures.To address these problems,this paper designs an integrated data glove capable of simultaneously collecting tactile and kinesthetic features,and proposes a data glove-mediated grasping skill transfer scheme.This scheme en-compasses a multimodal feature representation based on graph structures and polar coordinates,estimation of un-known contact forces under static force equilibrium assumptions,and a dynamic remapping method utilizing de-sired joint angles and contact force distributions.Experimental results demonstrate that for objects with diverse properties,such as deformable and irregular geometries,the proposed scheme achieves a high grasping success rate while maintaining proper contact force control,producing results that relatively more closely resemble direct hu-man grasping among the baseline schemes.