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Autonomous bin picking poses significant challenges to vision-driven roboticsystems given the complexity of the problem, ranging from various sensormodalities, to highly entangled object layouts, to diverse item properties andgripper types. Existing methods often address the problem from one perspective.Diverse items and complex bin scenes require diverse picking strategiestogether with advanced reasoning. As such, to build robust and effectivemachine-learning algorithms for solving this complex task requires significantamounts of comprehensive and high quality data. Collecting such data in realworld would be too expensive and time prohibitive and therefore intractablefrom a scalability perspective. To tackle this big, diverse data problem, wetake inspiration from the recent rise in the concept of metaverses, andintroduce MetaGraspNet, a large-scale photo-realistic bin picking datasetconstructed via physics-based metaverse synthesis. The proposed datasetcontains 217k RGBD images across 82 different article types, with fullannotations for object detection, amodal perception, keypoint detection,manipulation order and ambidextrous grasp labels for a parallel-jaw and vacuumgripper. We also provide a real dataset consisting of over 2.3k fully annotatedhigh-quality RGBD images, divided into 5 levels of difficulties and an unseenobject set to evaluate different object and layout properties. Finally, weconduct extensive experiments showing that our proposed vacuum seal model andsynthetic dataset achieves state-of-the-art performance and generalizes to realworld use-cases.

MetaGraspNet: A Large-Scale Benchmark Dataset for Scene-Aware Ambidextrous Bin Picking via Physics-based Metaverse Synthesis