Delicate industrial insertion tasks (e.g., PC board assembly) remain challenging for industrial robots. The challenges include low error tolerance, delicacy of the components, and large task variations with respect to the components to be inserted. To deliver a feasible robotic solution for these insertion tasks, we also need to account for hardware limits of existing robotic systems and minimize the integration effort. This paper proposes a composable framework for efficient integration of a safe insertion policy on existing robotic platforms to accomplish these insertion tasks. The policy has an interpretable modularized design and can be learned efficiently on hardware and transferred to new tasks easily. In particular, the policy includes a safe insertion agent as a baseline policy for insertion, an optimal configurable Cartesian tracker as an interface to robot hardware, a probabilistic inference module to handle component variety and insertion errors, and a safe learning module to optimize the parameters in the aforementioned modules to achieve the best performance on designated hardware. The experiment results on a UR10 robot show that the proposed framework achieves safety (for the delicacy of components), accuracy (for low tolerance), robustness (against perception error and component defection), adaptability and transferability (for task variations), as well as task efficiency during execution plus data and time efficiency during learning.
Rainbow Deep Q-Learning with Kinematics-Aware Design for Cooperative Delta and 3-RRS Parallel Robot Insertion
Robotics Research Center of Yuyao（余姚机器人研究中心）；Robotics Institute of Zhejiang University（浙江大学机器人院）；Yuyao Technology Innovation Center（余姚技术创新中心）；Department of Electrical and Computer Engineering, University of New Brunswick（新 Brunswick大学电气与计算机工程系）
Eversion-based robots can enable safe access,steering and endoscopic imaging within the spinal subarachnoid space
King’s College London（伦敦国王学院）；University of Thessaly（色萨利大学）；Queen Mary University London（伦敦玛丽女王大学）；Institute of Psychiatry, Psychology & Neuroscience, King’s College London（伦敦国王学院精神病学、心理学与神经科学研究所）
Safe Uncertainty-Aware Learning of Robotic Suturing
SDU Robotics, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark（SDU机器人研究所、马士基麦金尼莫勒研究所、南部丹麦大学）；Department of Mechanical Engineering, Myongji University（机械工程系、明州大学）
Safe Payload Transfer with Ship-Mounted Cranes: A Robust Model Predictive Control Approach
California Institute of Technology（加州理工学院）；Jet Propulsion Laboratory（喷气推进实验室）；Illinois Institute of Technology（伊利诺伊理工学院）；University of Southern California（南加州大学）；Stealth Labs
Universität Klagenfurt（克拉根福大学）；Alias Robotics；Institute for Robotics and Mechatronics, Joanneum Research（机器人与机电一体化研究所，格拉茨研究中心）；Department of Informatics of University of Minho（米尼奥大学信息学系）；High-Assurance Software Laboratory of University of Minho（米尼奥大学高可信软件实验室）；INESC TEC；Robotics Group at University of León（莱昂大学机器人学小组）；Istituto di Informatica e Telematica, Consiglio Nazionale delle Ricerche (CNR)（意大利国家研究委员会信息与电信研究所）
One Demonstration Is Enough for Real-World Robotic Reinforcement Learning
National Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institution of Automation, Chinese Academy of Sciences（中国科学院自动化研究所复杂系统认知与决策智能国家重点实验室）；Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）；PKU-PsiBot Joint Lab（北大-鹏城实验室联合实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）
