论文标题
通过生成模型学习多样化且具有物理可行的灵巧抓地力和双重优化
Learning Diverse and Physically Feasible Dexterous Grasps with Generative Model and Bilevel Optimization
论文作者
论文摘要
要充分利用多指的灵巧机器人手的多功能性来执行各种对象抓紧，必须考虑手动对象相互作用和对象几何形状引入的丰富物理约束。我们提出了一种组合生成模型和双重优化（BO）的综合方法，以计划对新物体的各种掌握配置。首先，仅在六个YCB对象上训练的条件变异自动编码器直接从对象点云中预测了手指的位置。然后，该预测用于播种一个非凸bo，该非凸bo求解在碰撞，可及性，扳手闭合和摩擦约束下的掌握配置。我们的方法在120个现实世界中取得了86.7％的成功，对20种家庭对象进行了掌握试验，包括看不见和具有挑战性的几何形状。通过定量的经验评估，我们确认我们的管道产生的掌握构型确实可以满足运动学和动态约束。我们的结果的视频摘要可在youtu.be/9dtrimbn99i上找到。
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.
