In this work, we present GraspFlow, a refinement approach for generating context-specific grasps. We formulate the problem of grasp synthesis as a sampling problem: we seek to sample from a context-conditioned probability distribution of successful grasps. However, this target distribution is unknown. As a solution, we devise a discriminator gradient-flow method to evolve grasps obtained from a simpler distribution in a manner that mimics sampling from the desired target distribution. Unlike existing approaches, GraspFlow is modular, allowing grasps that satisfy multiple criteria to be obtained simply by incorporating the relevant discriminators. It is also simple to implement, requiring minimal code given existing auto-differentiation libraries and suitable discriminators. Experiments show that GraspFlow generates stable and executable grasps on a real-world Panda robot for a diverse range of objects. In particular, in 60 trials on 20 different household objects, the first attempted grasp was successful 94% of the time, and 100% grasp success was achieved by the second grasp. Moreover, incorporating a functional discriminator for robot-human handover improved the functional aspect of the grasp by up to 33%.

翻译：本文提出GraspFlow，一种用于生成上下文特定抓取姿态的优化方法。我们将抓取合成问题建模为采样问题：目标是从成功抓取的条件概率分布中采样。然而，该目标分布未知。为此，我们设计了一种判别器梯度流方法，通过模拟从目标分布采样的方式，使由简单分布生成的抓取姿态逐步演化。与现有方法不同，GraspFlow具有模块化特性，只需整合相关判别器即可获得满足多重约束的抓取姿态。该方法实现简便，基于现成的自动微分库和合适的判别器仅需少量代码。实验表明，GraspFlow能在真实Panda机器人上为多种物体生成稳定可执行的抓取姿态。具体而言，在20种不同家用物体的60次试验中，首次抓取成功率达94%，第二次抓取成功率达100%。此外，集成机器人-人手递物的功能判别器可将抓取的功能性提升高达33%。