MetaGraspNet

2023

1. 

   ShapeShift: Superquadric-based Object Pose Estimation for Robotic Grasping

   E Zhixuan Zeng, Yuhao Chen , and Alexander Wong

   In WICV workshop , 2023

   Abs PDF

   Object pose estimation is a critical task in robotics for precise object manipulation. However, current techniques heavily rely on a reference 3D object, limiting their generalizability and making it expensive to expand to new object categories. Direct pose predictions also provide limited information for robotic grasping without referencing the 3D model. Keypoint-based methods offer intrinsic descriptiveness without relying on an exact 3D model, but they may lack consistency and accuracy. To address these challenges, this paper proposes ShapeShift, a superquadric-based framework for object pose estimation that predicts the object’s pose relative to a primitive shape which is fitted to the object. The proposed framework offers intrinsic descriptiveness and the ability to generalize to arbitrary geometric shapes beyond the training set.

2. 

   MMRNet: Improving Reliability for Multimodal Object Detection and Segmentation for Bin Picking via Multimodal Redundancy

   Yuhao Chen , Hayden Gunraj , E Zhixuan Zeng, and 3 more authors

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023

   Abs PDF

   Recently, there has been tremendous interest in industry 4.0 infrastructure to address labor shortages in global supply chains. Deploying artificial intelligence-enabled robotic bin picking systems in real world has become particularly important for reducing stress and physical demands of workers while increasing speed and efficiency of warehouses. To this end, artificial intelligence-enabled robotic bin picking systems may be used to automate order picking, but with the risk of causing expensive damage during an abnormal event such as sensor failure. As such, reliability becomes a critical factor for translating artificial intelligence research to real world applications and products. In this paper, we propose a reliable object detection and segmentation system with MultiModal Redundancy (MMRNet) for tackling object detection and segmentation for robotic bin picking using data from different modalities. This is the first system that introduces the concept of multimodal redundancy to address sensor failure issues during deployment. In particular, we realize the multimodal redundancy framework with a gate fusion module and dynamic ensemble learning. Finally, we present a new label-free multi-modal consistency (MC) score that utilizes the output from all modalities to measure the overall system output reliability and uncertainty. Through experiments, we demonstrate that in an event of missing modality, our system provides a much more reliable performance compared to baseline models. We also demonstrate that our MC score is a more reliability indicator for outputs during inference time compared to the model generated confidence scores that are often over-confident.

2022

1. 

   MetaGraspNet: A Large-Scale Benchmark Dataset for Vision-driven Robotic Grasping via Physics-based Metaverse Synthesis

   Yuhao Chen , Maximilian Gilles , E Zhixuan Zeng, and 1 more author

   In 2022 IEEE CASE , 2022

   Awareded Abs PDF

   Finalist

   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.

2. Investigating Use of Keypoints for Object Pose Recognition

   E Zhixuan Zeng, Yuhao Chen , and Alexander Wong

   In Journal of Computational Vision and Imaging Systems , 2022

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   Object pose detection is a task that is highly useful for a variety of object manipulation tasks such as robotic grasping and tool handling. Perspective-n-Point matching between keypoints on the objects offers a way to perform pose estimation where the keypoints also provide inherent object information, such as corner locations and object part sections, without the need to reference a separate 3D model. Existing works focus on scenes with little occlusion and limited object categories. In this study, we demonstrate the feasibility of a pose estimation network based on detecting semantically important keypoints on the MetagraspNet dataset which contains heavy occlusion and greater scene complexity. We further discuss various challenges in using semantically important keypoints as a way to perform object pose estimation. These challenges include maintaining consistent keypoint definition, as well as dealing with heavy occlusion and similar visual features.