To fully utilize the versatility of a multi-fingered dexterous robotic hand for executing diverse object grasps, one must consider the rich physical constraints introduced by hand-object interaction and object geometry. We propose an integrative approach of combining a generative model and a bilevel optimization (BO) to plan diverse grasp configurations on novel objects. First, a conditional variational autoencoder trained on merely six YCB objects predicts the finger placement directly from the object point cloud. The prediction is then used to seed a nonconvex BO that solves for a grasp configuration under collision, reachability, wrench closure, and friction constraints. Our method achieved an 86.7% success over 120 real world grasping trials on 20 household objects, including unseen and challenging geometries. Through quantitative empirical evaluations, we confirm that grasp configurations produced by our pipeline are indeed guaranteed to satisfy kinematic and dynamic constraints. A video summary of our results is available at youtu.be/9DTrImbN99I.

翻译：为了充分利用多指多指的多指的机器人手的多功能性,以实施多种物体的掌握,必须考虑到手工物体相互作用和物体几何学带来的大量物理限制。我们建议采用综合方法,将基因模型和双级优化(BO)相结合,以规划新物体的多种掌握配置。首先,仅用六个YCB天天体进行训练的有条件的变异自动编码器,直接从对象点云中预测手指的放置情况。然后,预测用于播种一个非Convex的BO,在碰撞、可达性、扳手封闭和摩擦限制下解决掌握配置问题。我们的方法在120个实际世界的20个住户物体(包括不可见的和具有挑战性的地貌)的试验中取得了86.7%的成功。我们通过定量经验评估确认,我们通过管道产生的掌握的配置确实能够满足运动和动态的制约。我们的成果的视频摘要可在您查询。be/9DGmbN99I。