In this article, we explore the use of deep neural networks for grasping objects in robotics. The authors propose a method called AdaGrasp, which learns to generate grasps by predicting the relative pose of interacting objects. The approach involves encoding objects or object parts using DGCNN (Deep Geometry Convolutional Neural Network) and learning a cross-attention model that predicts the relative poses of interacting objects. The scoring function for possible generated grasps is learned using 3D Convolutional Neural Networks, and the best grasp is executed. The proposed method is evaluated on a robotic arm with a multi-fingered hand, and the results show that AdaGrasp outperforms other state-of-the-art methods in terms of diversity and adaptability to different tasks.
Keypoint Sampling at Inference Time
Imagine you’re trying to pick up a delicate vase on a cluttered table. You wouldn’t simply grab the vase with your hand blindly, hoping it would fit. Instead, you’d carefully examine the situation, taking into account the position of the vase and other objects nearby. This is similar to what AdaGrasp does – it learns to grasp objects by predicting the relative pose of interacting objects before generating a grasp.
AdaGrasp uses a sampling algorithm called Beam Search to select the top-K points that are most likely to result in a successful grasp. However, for simplicity and computational efficiency, the authors use a more straightforward multimodal sampling approach that selects the top-0, 20, 50, and 100 points with highest scores. This allows them to explore a range of grasps while keeping the computational cost reasonable.
In summary, AdaGrasp learns to grasp objects by predicting the relative pose of interacting objects and selecting the most promising grasps using Beam Search or a simpler multimodal sampling approach. By doing so, it can adapt to different tasks and environments, leading to improved performance and versatility in robotic grasping.
