Learning Diverse and Physically Feasible Dexterous Grasps with Generative Model and Bilevel Optimization

A. Wu, Michelle Guo, Karen Liu
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引用次数: 10

Abstract

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.
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用生成模型和双层优化学习多种物理可行的灵巧掌握
为了充分利用多指灵巧机械手的多功能性来执行不同的物体抓取,必须考虑手-物交互和物体几何引入的丰富物理约束。我们提出了一种结合生成模型和双层优化(BO)的综合方法来规划新对象的不同抓取配置。首先,一个条件变分自编码器只训练了6个YCB对象,直接从对象点云预测手指的位置。然后使用该预测来生成一个非凸BO,该BO可解决碰撞、可达性、扳手闭合和摩擦约束下的抓握配置。我们的方法在20个家庭物体的120次真实世界抓取试验中取得了86.7%的成功率,包括看不见的和具有挑战性的几何形状。通过定量的经验评估,我们证实了我们的管道产生的抓取构型确实保证满足运动学和动力学约束。我们的研究结果的视频摘要可以在youtube .be/9DTrImbN99I上找到。
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