MetaGraspNetV2: All-in-One Dataset Enabling Fast and Reliable Robotic Bin Picking via Object Relationship Reasoning and Dexterous Grasping

Abstract

Grasping unknown objects in unstructured environments is one of the most challenging and demanding tasks for robotic bin picking systems. Developing a holistic approach is crucial to building such dexterous bin picking systems to meet practical requirements on speed, cost and reliability. Proposed datasets so far focus only on challenging sub-problems and are therefore limited in their ability to leverage the complementary relationship between individual tasks. In this paper, we tackle this holistic data challenge and design MetaGraspNetV2, an allin-one bin picking dataset consisting of (i) a photo-realistic dataset with over 296k images, which has been created through physics-based metaverse synthesis; and (ii) a real-world test dataset with 3.2k images featuring task-specific difficulty levels. Both datasets provide full annotations for amodal panoptic segmentation, object relationship detection, occlusion reasoning, 6-DoF pose estimation, and grasp detection for a parallel-jaw as well as a vacuum gripper. Extensive experiments demonstrate that our dataset outperforms state-of-the-art datasets in object detection, instance segmentation, amodal detection, parallel-jaw grasping, and vacuum grasping. Furthermore, leveraging the potential of our data for building holistic perception systems, we propose a single-shot-multi-pick (SSMP) grasping policy for scene understanding accelerated fast picking in high clutter. SSMP reasons about suitable manipulation orders for blindly picking multiple items given a single image acquisition. Physical robot experiments demonstrate that SSMP effectively speeds up cycle times through reducing image acquisitions by more than 47% while providing better grasp performance compared to state-of-the-art bin picking methods.