To fully utilize the versatility of a multi-finger dexterous robotic hand for object grasping, one must satisfy complex physical constraints introduced by hand-object interaction and object geometry during grasp planning. We propose an integrative approach of combining a generative model and a bilevel optimization to compute diverse grasps for novel unseen objects. First, a grasp prediction is obtained from a conditional variational autoencoder trained on merely six YCB objects. The prediction is then projected onto the manifold of kinematically and dynamically feasible grasps by jointly solving collision-aware inverse kinematics, force closure, and friction constraints as one nonconvex bilevel optimization. We demonstrate the effectiveness of our method on hardware by successfully grasping a wide range of unseen household objects, including adversarial shapes challenging to other types of robotic grippers. A video summary of our results is available at https://youtu.be/9DTrImbN99I.

翻译：为了充分利用多指、多指、多伸缩的机器人手的多功能来捕捉物体,人们必须满足人工物体相互作用和物体几何学在捕捉规划过程中带来的复杂的物理限制。我们建议采用综合方法,将基因模型和双级优化结合起来,以计算新看不见物体的多种捕捉。首先,从仅受过六YCB天天体培训的有条件的变异自动编码器中可以获得掌握的预测。然后,通过联合解决碰撞-对立运动、武力封闭和摩擦限制,将预测投向运动和动态可行的捕捉器的方方面面。我们通过成功掌握各种看不见的家用物体,包括对其他类型机械握手者的对抗形状,展示了我们的方法在硬件上的有效性。我们的成果的视频摘要见https://youtu.be/9DTTrimbN99I。