Robotic Grasp Manipulation Using Evolutionary Computing and Deep Reinforcement Learning
Center of Intelligent Robotics, Indian Institute of Information Technology, Allahabad（印度阿拉哈巴德信息技术学院智能机器人中心）；Indian Institute of Technology (Indian School of Mines), Dhanbad（印度丹巴德印度理工学院（印度矿业学院））
智能物体的抓取操作是机器人面临的一个挑战性问题。与机器人不同，人类几乎立即知道如何操纵物体进行抓取，这是由于多年来的学习。一个成年女性可以比一个孩子更熟练地抓住物体，因为多年来发展的学习技能，在当今的机器人抓取中缺乏这种技能，迫使它的表现远远低于人类物体抓取基准。在本文中，我们已经采取了发展的挑战，学习为基础的姿态估计分解成位置和方向学习的问题。更具体地说，对于抓取位置估计，我们探索了三种不同的方法-基于遗传算法（GA）的优化方法，以最小化计算的图像点和预测的末端执行器（EE）位置之间的误差，基于回归的方法（RM），其中机器人EE和图像点的收集的数据点已经用线性模型回归，伪逆（PI）模型，其以具有机器人EE位置和用于若干观测的图像点的映射矩阵的形式被制定。此外，对于抓取方向学习，我们开发了一个深度强化学习（DRL）模型，我们将其命名为Grasp Deep Q-Network（GDQN），并将我们的结果与Modified VGG 16（MVGG 16）进行了基准测试。严格的实验表明，由于固有的能力，产生非常高质量的解决方案的优化问题和搜索问题，基于遗传算法的预测器的性能远远优于其他两个模型的位置估计。对于方向学习，结果表明，通过GDQN的离策略学习优于MVGG 16，因为GDQN架构是专门为强化学习而设计的。基于我们提出的架构和算法，机器人是能够抓住所有具有规则形状的刚体对象。
英文摘要：
Intelligent Object manipulation for grasping is a challenging problem for robots. Unlike robots, humans almost immediately know how to manipulate objects for grasping due to learning over the years. A grown woman can grasp objects more skilfully than a child because of learning skills developed over years, the absence of which in the present day robotic grasping compels it to perform well below the human object grasping benchmarks. In this paper we have taken up the challenge of developing learning based pose estimation by decomposing the problem into both position and orientation learning. More specifically, for grasp position estimation, we explore three different methods - a Genetic Algorithm (GA) based optimization method to minimize error between calculated image points and predicted end-effector (EE) position, a regression based method (RM) where collected data points of robot EE and image points have been regressed with a linear model, a PseudoInverse (PI) model which has been formulated in the form of a mapping matrix with robot EE position and image points for several observations. Further for grasp orientation learning, we develop a deep reinforcement learning (DRL) model which we name as Grasp Deep Q-Network (GDQN) and benchmarked our results with Modified VGG16 (MVGG16). Rigorous experimentations show that due to inherent capability of producing very high-quality solutions for optimization problems and search problems, GA based predictor performs much better than the other two models for position estimation. For orientation learning results indicate that off policy learning through GDQN outperforms MVGG16, since GDQN architecture is specially made suitable for the reinforcement learning. Based on our proposed architectures and algorithms, the robot is capable of grasping all rigid body objects having regular shapes.
GAMMA: Graspability-Aware Mobile MAnipulation Policy Learning based on Online Grasping Pose Fusion
CFCS, School of Computer Science, Peking University（北京大学计算机学院CFCS）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Galbot Tongji University（同济大学Galbot）
Research Challenges and Progress in Robotic Grasping and Manipulation Competitions
University of South Florida（南佛罗里达大学）；National Institute of Standards and Technology (NIST)（美国国家标准与技术研究院）；German Aerospace Center (DLR)（德国航空航天中心）；Worcester Polytechnic Institute (WPI)（伍斯特理工学院）
DynaMOMA: Instantaneous Prediction of Grasp Poses for Mobile Manipulation of Dynamic Objects
National University of Defense Technology（国防科技大学）；CFCS, Peking University（北京大学前沿计算研究中心）；Defense Innovation Institute, Academy of Military Sciences（军事科学院国防科技创新研究院）；Wuhan University（武汉大学）；Institute of AI for Industries, Chinese Academy of Sciences（中国科学院产业人工智能研究院）
Multi-Task Reinforcement Learning based Mobile Manipulation Control for Dynamic Object Tracking and Grasping
Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）；Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences（中国科学院机器人与智能制造创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Heriot-Watt University（赫瑞-瓦特大学）
GenVid2Robot: From Video Generation to Robot Manipulation via Rigid-Geometric Consistency
School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；University of Liverpool（利物浦大学）；Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算学系）；State Key Laboratory of Submarine Geoscience, School of Automation and Intelligent Sensing, Shanghai Jiao Tong University（上海交通大学海洋地球科学国家重点实验室，自动化与智能感知学院）
Language-Guided Grasping under Partial Observation for Mobile Manipulation in Field Inspection and Maintenance
Department of Mechanical Engineering, Federal University of Uberlândia（联邦大学伯南迪利亚机械工程系）；Department of Mechanical Engineering, University of São Paulo（圣保罗大学机械工程系）；Graduate School of Information Sciences, Tohoku University（东北大学信息科学研究生院）；Faculdade Israelita de Ensino e Pesquisa Albert Einstein, Hospital Israelita Albert Einstein（艾伯特·爱因斯坦以色列教学与研究学院，艾伯特·爱因斯坦医院）
Developing Modular Grasping and Manipulation Pipeline Infrastructure to Streamline Performance Benchmarking
New England Robotics Validation and Experimentation (NERVE) Center, University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；Rutgers University（罗格斯大学）；Worcester Polytechnic University（伍斯特理工大学）；Yale University（耶鲁大学）；University of South Florida（南佛罗里达大学）
PartDexTOG: Generating Dexterous Task-Oriented Grasping via Language-driven Part Analysis
College of Computer Science and Technology, National University of Defense Technology（计算机科学与技术学院，国防科技大学）；College of Intelligence Science and Technology, National University of Defense Technology（智能科学与技术学院，国防科技大学）
