In this work, we tackle the problem of learning universal robotic dexterous
grasping from a point cloud observation under a table-top setting. The goal is
to grasp and lift up objects in high-quality and diverse ways and generalize
across hundreds of categories and even the unseen. Inspired by successful
pipelines used in parallel gripper grasping, we split the task into two stages:
1) grasp proposal (pose) generation and 2) goal-conditioned grasp execution.
For the first stage, we propose a novel probabilistic model of grasp pose
conditioned on the point cloud observation that factorizes rotation from
translation and articulation. Trained on our synthesized large-scale dexterous
grasp dataset, this model enables us to sample diverse and high-quality
dexterous grasp poses for the object point cloud.For the second stage, we
propose to replace the motion planning used in parallel gripper grasping with a
goal-conditioned grasp policy, due to the complexity involved in dexterous
grasping execution. Note that it is very challenging to learn this highly
generalizable grasp policy that only takes realistic inputs without oracle
states. We thus propose several important innovations, including state
canonicalization, object curriculum, and teacher-student distillation.
Integrating the two stages, our final pipeline becomes the first to achieve
universal generalization for dexterous grasping, demonstrating an average
success rate of more than 60\% on thousands of object instances, which
significantly outperforms all baselines, meanwhile showing only a minimal
generalization gap.

本研究提出使用点云观测学习通用的机器人灵巧抓取，将任务分为抓取提案生成和目标条件抓取执行两个阶段，并使用目标条件抓取策略取代运动规划以提高通用性，在大规模机器人灵巧抓取数据集的平凡模型下，通过采样生成高质量多样化的抓取姿势，实现了普适性的抓取方法。