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Recently, robotic grasp detection (GD) and object detection (OD) withreasoning have been investigated using deep neural networks (DNNs). There havebeen works to combine these multi-tasks using separate networks so that robotscan deal with situations of grasping specific target objects in the cluttered,stacked, complex piles of novel objects from a single RGB-D camera. We proposea single multi-task DNN that yields the information on GD, OD and relationshipreasoning among objects with a simple post-processing. Our proposed methodsyielded state-of-the-art performance with the accuracy of 98.6% and 74.2% andthe computation speed of 33 and 62 frame per second on VMRD and Cornelldatasets, respectively. Our methods also yielded 95.3% grasp success rate forsingle novel object grasping with a 4-axis robot arm and 86.7% grasp successrate in cluttered novel objects with a Baxter robot.

A Single Multi-Task Deep Neural Network with Post-Processing for Object Detection with Reasoning and Robotic Grasp Detection