Double-Dot Network for Antipodal Grasp Detection
This paper proposes a new deep learning approach to antipodal grasp detection, named Double-Dot Network (DD-Net). It follows the recent anchor-free object detection framework, which does not depend on empirically pre-set anchors and thus allows more generalized and flexible prediction on unseen objects. Specifically, unlike the widely used 5-dimensional rectangle, the gripper configuration is defined as a pair of fingertips. An effective CNN architecture is introduced to localize such fingertips, and with the help of auxiliary centers for refinement, it accurately and robustly infers grasp candidates. Additionally, we design a specialized loss function to measure the quality of grasps, and in contrast to the IoU scores of bounding boxes adopted in object detection, it is more consistent to the grasp detection task. Both the simulation and robotic experiments are executed and state of the art accuracies are achieved, showing that DD-Net is superior to the counterparts in handling unseen objects.
Code (0)
등록된 구현이 없습니다.
Tasks
object-detectionObject DetectionSimilar Papers 제목 키워드 기반
Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network
In this paper, we present a modular robotic system to tackle the problem of generating and performing antipodal robotic grasps for unknown objects from n-channel image of the scene. We propose a novel Generative Residual…
Robotic GraspingMetaGrasp: Data Efficient Grasping by Affordance Interpreter Network
Data-driven approach for grasping shows significant advance recently. But these approaches usually require much training data. To increase the efficiency of grasping data collection, this paper presents a novel grasp tra…
VLAD-Grasp: Zero-shot Grasp Detection via Vision-Language Models
Robotic grasping is a fundamental capability for enabling autonomous manipulation, with usually infinite solutions. State-of-the-art approaches for grasping rely on learning from large-scale datasets comprising expert an…
Zero-shot GeneralizationRobotic GraspingPoint CloudsObject Pose and Shape Estimation for Grasping: Does it Work?
The problem of object pose and shape estimation has seen key advancements lately. Encoder-decoder (e.g., SAM3D, LRM, CRISP) and diffusion-based models (e.g., InstantMesh, Zero123, SceneComplete) have shown category-agnos…
Evaluating Gaussian Grasp Maps for Generative Grasping Models
Generalising robotic grasping to previously unseen objects is a key task in general robotic manipulation. The current method for training many antipodal generative grasping models rely on a binary ground truth grasp map …
ObjectRobotic GraspingTransfer Learning