Pixel-Reasoning-Based Robotics Fine Grasping for Novel Objects with Deep EDINet Structure

Robotics grasp detection has mostly used the extraction of candidate grasping rectangles; those discrete sampling methods are time-consuming and may ignore the potential best grasp synthesis. This paper proposes a new pixel-level grasping detection method on RGB-D images. Firstly, a fine grasping representation is introduced to generate the gripper configurations of parallel-jaw, which can effectively resolve the gripper approaching conflicts and improve the applicability to unknown objects in cluttered scenarios. Besides, the adaptive grasping width is used to adaptively represent the grasping attribute, which is fine for objects. Then, the encoder-decoder-inception convolution neural network (EDINet) is proposed to predict the fine grasping configuration. In our findings, EDINet uses encoder, decoder, and inception modules to improve the speed and robustness of pixel-level grasping detection. The proposed EDINet structure was evaluated on the Cornell and Jacquard dataset; our method achieves 98.9% and 96.1% test accuracy, respectively. Finally, we carried out the grasping experiment on the unknown objects, and the results show that the average success rate of our network model is 97.2% in a single object scene and 93.7% in a cluttered scene, which out-performs the state-of-the-art algorithms. In addition, EDINet completes a grasp detection pipeline within only 25 ms.

Automated summary

This paper proposes a new pixel-level grasping detection method on RGB-D images, which achieves high test accuracy in unknown object scenes and cluttered scenes through the introduction of fine grasping representation and adaptive grasping width, combined with the EDINet structure to predict detailed grasping configurations. It out-performs existing algorithms in grasping experiments on unknown objects, with an average success rate of 97.2% in a single object scene and 93.7% in a cluttered scene.