Abstract: A method for detecting a grasping position of a robot in grasping a target object includes: collecting a target RGB image and a target Depth image of the target object at different view angles; inputting each of the target RGB image to a target object segmentation network for calculation to obtain an RGB pixel region of the target object in the target RGB image and a Depth pixel region of the target object; inputting the RGB pixel region to an optimal grasping position generation network to obtain an optimal grasping position for grasping the target object; inputting the Depth pixel region of the target object and the optimal grasping position to a grasping position quality evaluation network to calculate a score of the optimal grasping position; and selecting an optimal grasping position corresponding to a highest score as a global optimal grasping position of the robot.

Abstract: The present disclosure discloses a data generation method and apparatus, and a computer-readable storage medium, the method including: importing a robot model by using a game engine; simulating a Red-Green-Blue Depth (RGBD) camera by a scene capture component in the game engine; controlling a human hand of the imported robot model to move within a field of view of the RGBD camera by using a joint control module in the game engine; acquiring RGBD image data by using the RGBD camera; and generating an annotated data set with coordinates of 21 key points according to the RGBD image data and coordinate information of a 3D pose of the 21 key points.

Abstract: A method for detecting a grasping position of a robot in grasping a target object includes: collecting a target RGB image and a target Depth image of the target object at different view angles; inputting each of the target RGB image to a target object segmentation network for calculation to obtain an RGB pixel region of the target object in the target RGB image and a Depth pixel region of the target object; inputting the RGB pixel region to an optimal grasping position generation network to obtain an optimal grasping position for grasping the target object; inputting the Depth pixel region of the target object and the optimal grasping position to a grasping position quality evaluation network to calculate a score of the optimal grasping position; and selecting an optimal grasping position corresponding to a highest score as a global optimal grasping position of the robot.