In this paper we investigate how to effectively deploy deep learning in practical industrial settings, such as robotic grasping applications. When a deep-learning based solution is proposed, usually lacks of any simple method to generate the training data. In the industrial field, where automation is the main goal, not bridging this gap is one of the main reasons why deep learning is not as widespread as it is in the academic world. For this reason, in this work we developed a system composed by a 3-DoF Pose Estimator based on Convolutional Neural Networks (CNNs) and an effective procedure to gather massive amounts of training images in the field with minimal human intervention. By automating the labeling stage, we also obtain very robust systems suitable for production-level usage. An open source implementation of our solution is provided, alongside with the dataset used for the experimental evaluation.
MG-Grasp: Metric-Scale Geometric 6-DoF Grasping Framework with Sparse RGB Observations
The Department of Electronic Engineering, Tsinghua University, Beijing 100084, China（清华大学电子工程系，北京100084，中国）；The Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China（香港科学与技术大学电子与计算机工程系，香港，中国）
You Only Scan Once: A Dynamic Scene Reconstruction Pipeline for 6-DoF Robotic Grasping of Novel Objects
Advanced Robotics Centre, National University of Singapore（新加坡国立大学先进机器人中心）；Integrative Sciences and Engineering Programme, National University of Singapore Graduate School（新加坡国立大学研究生院整合科学与工程项目）
Generalizing 6-DoF Grasp Detection via Domain Prior Knowledge
State Key Laboratory of Software Development Environment, Beihang University（北京航空航天大学软件开发环境国家重点实验室）；School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）
Learning 6-DoF Fine-grained Grasp Detection Based on Part Affordance Grounding
Center for X-Mechanics, School of Aeronautics and Astronautics, Zhejiang University（浙江大学航空航天学院X力学中心）；Information Hub, Data Science and Analytics Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）信息中心、数据科学与分析方向）；Advanced Manufacturing Lab, Huawei Technologies（华为技术先进制造实验室）；Research Institute, UBTECH Robotics Inc.（UBTECH机器人研究院）；School of Information and School of Smart Governance, Renmin University of China（中国人民大学信息学院和智能治理学院）；School of Engineering, Westlake University and the Institute of Advanced Technology, Westlake Institute for Advanced Study（西湖大学工程学院和西湖先进科技研究院）；Department of Electrical and Computer Engineering, Dalhousie University（达尔豪斯大学电气与计算机工程系）
PCF-Grasp: Converting Point Completion to Geometry Feature to Enhance 6-DoF Grasp
State Key Laboratory of Robotics and System at Harbin Institute of Technology（哈尔滨工业大学机器人系统国家重点实验室）；Lanzhou University of Technology（兰州理工大学）；Department of Computer Science（计算机科学系）；University of Liverpool（利物浦大学）
DexRepNet: Learning Dexterous Robotic Grasping Network with Geometric and Spatial Hand-Object Representations
College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）；Key Lab of CS&AUS of Zhejiang Province（浙江省计算机与应用系统安全重点实验室）；Department of Computer Science, National University of Singapore（新加坡国立大学计算机科学系）
Learning Tri-mode Grasping for Ambidextrous Robot Picking
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人中心）
Visual-tactile manipulation to collect household waste in outdoor
Instituto Universitario de Investigación Informática, Universidad de Alicante（阿利坎特大学大学信息学研究学院）；Universidad de Alicante（阿利坎特大学）；Universidad Miguel Hernández（米格尔·埃尔南德斯大学）
Triplane Grasping: Efficient 6-DoF Grasping with Single RGB Images
Computer Science Department, Faculty of Science and Engineering, Swansea University（计算机科学系，科学与工程学院，斯旺西大学）；Department of Computer Science, Durham University（计算机科学系，达勒姆大学）
Sequential Multi-Object Grasping with One Dexterous Hand
Thomas Lord Department of Computer Science, University of Southern California, USA（美国南加州大学计算机科学系）；Fudan University, China（复旦大学）；INSAIT, Sofia University “St. Kliment Ohridski”, Bulgaria（保加利亚索菲亚大学“圣克莱孟·奥赫里茨基”学院）
Scalable Unseen Objects 6-DoF Absolute Pose Estimation with Robotic Integration
National Engineering Research Center for Robot Visual Perception and Control Technology, School of Artificial Intelligence and Robotics, Hunan University（机器人视觉感知与控制技术国家工程研究中心，人工智能与机器人学院，湖南大学）；School of Architecture and Art, Central South University（建筑与艺术学院，中南大学）；School of Computing and Communications, Lancaster University（计算与通讯学院，兰卡斯特大学）；Department of Computer Science and Software Engineering, The University of Western Australia（计算机科学与软件工程系，西澳大学）；School of Electrical and Electronic Engineering, Nanyang Technological University（电子与电气工程学院，南洋理工大学）
Lightweight Learning from Actuation-Space Demonstrations via Flow Matching for Whole-Body Soft Robotic Grasping
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）；University of Science and Technology of China (USTC)（中国科学技术大学）；University of Freiburg（弗莱堡大学）；Ludwig Maximilian University of Munich (LMU Munich)（慕尼黑大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
