This study combines data generation and sim-to-real transfer learning in a grasping framework that reduces the sim-to-real gap and enables precise and reliable model-free grasping. Most of the current methods for model-free grasping are disadvantaged by the sparse data in grasping datasets and by errors in sensor data and contact models. Precise robotic grasping of several novel objects is a huge challenge in manufacturing, automation, and logistics. In addition, challenges about vision-based robotic grasping and future directions in addressing these challenges are also pointed out. Related datasets and comparisons between state-of-the-art methods are summarized as well. Both traditional methods and latest deep learning-based methods based on the RGB-D image inputs are reviewed elaborately in this survey. ![]() Lots of grasp estimation methods need not object localization and object pose estimation, and they conduct grasp estimation in an end-to-end manner. Lots of object pose estimation methods need not object localization, and they conduct object localization and object pose estimation jointly. ![]() These three tasks could accomplish the robotic grasping with different combinations. The grasp estimation task includes 2D planar grasp methods and 6DoF grasp methods, where the former is constrained to grasp from one direction. The object pose estimation task mainly refers to estimating the 6D object pose and includes correspondence-based methods, template-based methods and voting-based methods, which affords the generation of grasp poses for known objects. ![]() This task provides the regions of the target object in the input data. In detail, the object localization task contains object localization without classification, object detection and object instance segmentation. We conclude three key tasks during vision-based robotic grasping, which are object localization, object pose estimation and grasp estimation. This paper presents a comprehensive survey on vision-based robotic grasping.
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