Abstract
The field of robotic grasping has seen significant progress with the development of deep learning and the creation of large-scale datasets like the Cornell Grasping Dataset (Jiang et al., 2011) and DexNet (Mahler et al., 2016). However, challenges persist due to the reliance on manually annotated datasets, limited by data scarcity, high costs, biases, and a lack of diversity in gripper types and three-dimensional information, hampering their effectiveness in real-world applications. To confront these issues, an innovative method was introduced for generating robotic grasping datasets in a simulated environment, eliminating the need for manual annotations. The method utilizes a highly realistic movement of the gripper, offering extensive customization options for a variety of gripper types. It also introduces detailed evaluation metrics specifically designed to assess different gripper designs, ensuring accurate and meaningful analysis of grasping efficacy. Further, it excels in simulating a wide range of industrial scenarios, significantly enhancing the dataset's diversity and applicability in real-world applications. In addition, an end-to-end grasping prediction network is introduced, which leverages advanced graph convolution techniques to predict optimal grasping points and orientations from point cloud. It also serves as an effective baseline for the proposed grasping dataset. Lastly, the authors propose a novel interactive training method for deep learning models driven by data generation, featuring real-time interaction between the model and the data generator with a rule-based strategy that optimizes the training workflow based on feedback. Experimental results demonstrate that the interactive training method enables models to achieve superior outcomes in a shorter timeframe compared to those trained using traditional methods.
Published Version
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