This paper proposes a Bayes Net based Monte Carlo optimization for motion planning (BN-MCO). Primarily, we adjust the potential fields determined by goal and start constraints to progressively guide the sampled clusters toward the goal and start points. Then, we utilize the Gaussian mixed modal (GMM) to perform the Monte Carlo optimization, confronting these two non-convex potential fields. Moreover, KL divergence measures the bias between the true distribution determined by the fields and the proposed GMM, whose parameters are learned incrementally according to the manifold information of the bias. In this way, the Bayesian network consisting of sequential updated GMMs expands until the constraints are satisfied and the shortest path method can find a feasible path. Finally, we tune the key parameters and benchmark BN-MCO against the other 5 planners on LBR-iiwa in a bookshelf. The result shows the highest success rate and moderate solving efficiency of BN-MCO.
Dynamically Efficient Kinematics for Hyper-Redundant Manipulators
Computer Science and Engineering Department, University of South Carolina（南卡罗来纳大学计算机科学与工程系）；Department of Mechanical Engineering, National Technical University of Athens（雅典国立技术大学机械工程系）
Dynamic Programming-Based Offline Redundancy Resolution of Redundant Manipulators Along Prescribed Paths with Real-Time Adjustment
School of Mathematical Sciences, Zhejiang University（浙江大学数学科学学院）；Zhejiang Demetics Medical Technology Co., Ltd.（浙江迪美特斯医疗科技有限公司）；Teachers College, Columbia University（哥伦比亚大学教师学院）；Zhejiang College of Sports（浙江体育学院）；Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China（电子科技大学深圳高等研究院）
Dynamic Programming-Based Redundancy Resolution for Path Planning of Redundant Manipulators Considering Breakpoints
School of Mathematical Sciences, Zhejiang University（浙江大学数学科学学院）；Zhejiang Demetics Medical Technology Co., Ltd.（浙江德美特斯医疗科技有限公司）；Teachers College, Columbia University（哥伦比亚大学教师学院）；Zhejiang College of Sports（浙江体育学院）；Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China（电子科技大学深圳高等研究院）
RobotDiffuse: Diffusion-Based Motion Planning for Redundant Manipulators with the ROP Obstacle Avoidance Dataset
School of Computer Science and Engineering, Beihang University, Beijing, China（北京航空航天大学计算机科学与工程学院）；School of Software, Beihang University, Beijing, China（北京航空航天大学软件学院）；Hangzhou Innovation Institute, Beihang University, Hangzhou, China（北京航空航天大学杭州创新研究院）
A Task-Driven, Planner-in-the-Loop Computational Design Framework for Modular Manipulators
Humanoids and Human Centered Mechatronics Research Line, Italian Institute of Technology (IIT) Genoa, Italy（意大利理工学院（IIT）热那亚人类与以人为中心的机电研究组）；Department of Industrial Engineering, University of Trento, Trento, Italy（特伦托大学工业工程系）
Multimodal Control of Manipulators: Coupling Kinematics and Vision for Self-Driving Laboratory Operations
1 Department of Electronics Systems, Aalborg University, Denmark；3 Extreme Robotics Laboratory, School of Metallurgy \& Materials, University of Birmingham, Birmingham, United Kingdom
Real-Time Conformal-Seeded Hybrid Inverse Kinematics for Offset Redundant Manipulators
VinRobotics；Nanyang Technological University（南洋理工大学）；VinRobotics JSC（VinRobotics股份公司）；Center for AI Research (CAIR), VinUniversity（VinUniversity人工智能研究中心）
Autonomous Underwater Vehicle-Manipulator Systems Path Planning with RRTAUVMS Algorithm
Zhejiang University（浙江大学）；Shenzhen Sensetime Technology CO.,LTD（深圳商汤科技有限公司）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
URPlanner: A Universal Paradigm For Collision-Free Robotic Motion Planning Based on Deep Reinforcement Learning
Integrative Sciences and Engineering Programme, NUS Graduate School, National University of Singapore（国家大学学院整合科学与工程计划）；Advanced Robotics Centre, National University of Singapore（国家大学新加坡先进机器人中心）；Department of Mechanical Engineering, College of Design and Engineering, National University of Singapore（设计与工程学院机械工程系）；Singapore MIT Alliance for Research and Technology (SMART) centre（新加坡MIT研究与技术联盟中心）
Sensor-Space Based Robust Kinematic Control of Redundant Soft Manipulator by Learning
Department of Mechanical and Aerospace Engineering, The University of Manchester, Manchester, United Kingdom（机械与航空航天工程系，曼彻斯特大学，英国曼彻斯特）；Department of Electrical and Electronic Engineering, The University of Manchester, Manchester, United Kingdom（电气与电子工程系，曼彻斯特大学，英国曼彻斯特）
Learning Cross-Coupled and Regime Dependent Dynamics for Aerial Manipulation
Department of Computer Science, The University of Manchaster（曼彻斯特大学计算机科学系）；International Institute of Information Technology Hyderabad（海得拉巴国际信息科技研究所）；Department of Cognitive Robotics, Delft University of Technology（代尔夫特理工大学认知机器人系）；Newcastle University（新castle大学）
Computing forward statics from tendon-length in flexible-joint hyper-redundant manipulators
Institute for Integrated Micro and Nano Systems, School of Engineering, University of Edinburgh（整合微纳系统研究所，工程学院，爱丁堡大学）；CREATE Lab, EPFL（EPFL CREATE 实验室）；Institute for Imaging, Data and Communications, School of Engineering, University of Edinburgh（成像、数据与通信研究所，工程学院，爱丁堡大学）
Design and Control of a Highly Redundant Rigid-Flexible Coupling Robot to Assist the COVID-19 Oropharyngeal-Swab Sampling
Technische Universität München（慕尼黑工业大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）
