  Autonomous bin picking poses significant challenges to vision-driven robotic
systems given the complexity of the problem, ranging from various sensor
modalities, to highly entangled object layouts, to diverse item properties and
gripper types. Existing methods often address the problem from one perspective.
Diverse items and complex bin scenes require diverse picking strategies
together with advanced reasoning. As such, to build robust and effective
machine-learning algorithms for solving this complex task requires significant
amounts of comprehensive and high quality data. Collecting such data in real
world would be too expensive and time prohibitive and therefore intractable
from a scalability perspective. To tackle this big, diverse data problem, we
take inspiration from the recent rise in the concept of metaverses, and
introduce MetaGraspNet, a large-scale photo-realistic bin picking dataset
constructed via physics-based metaverse synthesis. The proposed dataset
contains 217k RGBD images across 82 different article types, with full
annotations for object detection, amodal perception, keypoint detection,
manipulation order and ambidextrous grasp labels for a parallel-jaw and vacuum
gripper. We also provide a real dataset consisting of over 2.3k fully annotated
high-quality RGBD images, divided into 5 levels of difficulties and an unseen
object set to evaluate different object and layout properties. Finally, we
conduct extensive experiments showing that our proposed vacuum seal model and
synthetic dataset achieves state-of-the-art performance and generalizes to real
world use-cases.
