There is an increased demand for task automation in robots. Contact-rich tasks, wherein multiple contact transitions occur in a series of operations, are extensively being studied to realize high accuracy. In this study, we propose a methodology that uses reinforcement learning (RL) to achieve high performance in robots for the execution of assembly tasks that require precise contact with objects without causing damage. The proposed method ensures the online generation of stiffness matrices that help improve the performance of local trajectory optimization. The method has an advantage of rapid response owing to short sampling time of the trajectory planning. The effectiveness of the method was verified via experiments involving two contact-rich tasks. The results indicate that the proposed method can be implemented in various contact-rich manipulations. A demonstration video shows the performance. (https://youtu.be/gxSCl7Tp4-0)
Co-optimizing Physical Reconfiguration Parameters and Controllers for an Origami-inspired Reconfigurable Manipulator
Colorado State University（科罗拉多州立大学）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Manning College of Information and Computer Sciences（曼宁信息与计算机科学学院）
Simulation-based planning of Motion Sequences for Automated Procedure Optimization in Multi-Robot Assembly Cells
Institute for Material Handling and Logistics (IFL), Karlsruhe Institute of Technology（材料搬运与物流研究所（IFL），卡尔斯鲁厄理工学院）；Bosch Corporate Research, Robert Bosch GmbH（博世企业研究，罗伯特·博世有限公司）
A Novel Aerial-Aquatic Locomotion Robot with Variable Stiffness Propulsion Module
UC Center for Robotics Research, College of Engineering and Applied Science, University of Cincinnati（辛辛那提大学工程与应用科学学院UC机器人研究中心）；Robotics Institute, School of Computer Science, Carnegie Mellon University（卡内基梅隆大学计算机学院机器人研究所）；Mohamed bin Zayed U（穆罕默德·本·扎耶德大学）
Ultra-slender Coaxial Antagonistic Tubular Robot for Ambidextrous Manipulation
West China Hospital of Medicine, Sichuan University（四川大学华西医学院）；Shien-Ming Wu School of Intelligent Engineering, South China University of Technology（华南理工大学伍舜明智能工程学院）；School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China（电子科技大学机械与电气工程学院）；School of Mechanical Engineering, Sichuan University（四川大学机械工程学院）；Centre for Artificial Intelligence and Robotics (CAIR) 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）
Trajectory-Optimized Sensing for Active Search of Tissue Abnormalities in Robotic Surgery
Robotics Institute at Carnegie Mellon University（卡内基梅隆大学机器人学院）；Machine Learning Department at Carnegie Mellon University（卡内基梅隆大学机器学习系）；Mechanical Engineering Department at Vanderbilt University（范德堡大学机械工程系）
ALBATROSS: A robotised system for high-throughput electrolyte screening via automated electrolyte formulation, coin-cell fabrication, and electrochemical evaluation
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院（KAIST））；LG Energy Solution（LG新能源）
