Recent years have seen the emergence of non-cooperative objects in space, like failed satellites and space junk. These objects are usually operated or collected by free-float dual-arm space manipulators. Thanks to eliminating the difficulties of modeling and manual parameter-tuning, reinforcement learning (RL) methods have shown a more promising sign in the trajectory planning of space manipulators. Although previous studies demonstrate their effectiveness, they cannot be applied in tracking dynamic targets with unknown rotation (non-cooperative objects). In this paper, we proposed a learning system for motion planning of free-float dual-arm space manipulator (FFDASM) towards non-cooperative objects. Specifically, our method consists of two modules. Module I realizes the multi-target trajectory planning for two end-effectors within a large target space. Next, Module II takes as input the point clouds of the non-cooperative object to estimate the motional property, and then can predict the position of target points on an non-cooperative object. We leveraged the combination of Module I and Module II to track target points on a spinning object with unknown regularity successfully. Furthermore, the experiments also demonstrate the scalability and generalization of our learning system.
Motion Planning of Cooperative Nonholonomic Mobile Manipulators
Department of Mechanical Engineering, Indian Institute of Technology Bombay（印度理工学院班加罗尔机械工程系）；Center for Systems and Control, Indian Institute of Technology Bombay（印度理工学院班加罗尔系统控制中心）
Trajectory Planning for Teleoperated Space Manipulators Using Deep Reinforcement Learning
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Research Institute of Tsinghua University in Shenzhen（清华大学深圳研究院）；Department of Automation, Tsinghua University（清华大学自动化系）
Motion Planning for Multi-Mobile-Manipulator Payload Transport Systems
IIIT Hyderabad（国际信息技术研究所海得拉巴分校）；University of Genoa（热那亚大学）；University of Tübingen（图宾根大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；IIT Jodhpur（焦特布尔印度理工学院）
Reinforcement Learning with Prior Policy Guidance for Motion Planning of Dual-Arm Free-Floating Space Robot
Beijing Institute of Control Engineering（北京控制工程研究所）；City University of Hong Kong（香港城市大学）；Qian Xuesen Laboratory of Space Technology（钱学森空间技术实验室）；Tsinghua University（清华大学）
Optimal Trajectory Planning for Orbital Robot Rendezvous and Docking
Space Robotics Lab. (SRL) in the Department of Aerospace Engineering, Graduate School of Engineering, Tohoku University（空间机器人实验室（SRL）位于工程研究生院航空航天工程系，东北大学）
HCLM: A Hierarchical Framework for Cooperative Loco-Manipulation with Dual Quadrupeds
Shenzhen International Graduate School, Tsinghua University, Shenzhen, China（深圳国际研究生院，清华大学，深圳，中国）；Department of Electronic Engineering, and the Institute for Embodied Intelligence and Robotics, Tsinghua University, Beijing, China（电子工程系，以及 embodied intelligence and robotics 院，清华大学，北京，中国）
Online Inertia Parameter Estimation for Unknown Objects Grasped by a Manipulator Towards Space Applications
Space Robotics Lab. (SRL) in the Department of Aerospace Engineering, Graduate School of Engineering, Tohoku University（太空机器人实验室（SRL）位于东京大学工学研究生院航空航天工程系）；The Space Robotics Research Group at the Interdisciplinary Research Center for Security reliability and Trust (SnT) in the University of Luxembourg（卢森堡大学安全可靠性与信任跨学科研究中心的太空机器人研究组）
A Supervisory Learning Control Framework for Autonomous & Real-time Task Planning for an Underactuated Cooperative Robotic task
Department of Electromechanical, Systems and Metal Engineering, Ghent University（根特大学机电、系统与金属工程系）；Core Lab MIRO, Flanders Make Strategic Research Centre for the Manufacturing Industry（弗拉芒制造战略研究中心MIRO核心实验室）
Cooperative Adaptive Control for Cloud-Based Robotics
Dept. Aerospace and Mech. Engineering, University of Notre Dame（美国圣母大学航空航天与机械工程系）；Dept. of Mech. Engineering, Massachusetts Institute of Technology（麻省理工学院机械工程系）
Task Capability Improvement Algorithm for Collaborative Manipulators
Department of Mechanical Engineering, Indian Institute of Technology Bombay（印度理工学院班加罗尔机械工程系）；Center for Systems and Control, Indian Institute of Technology Bombay（印度理工学院班加罗尔系统与控制中心）
Safe Obstacle-Free Guidance of Space Manipulators in Debris Removal Missions via Deep Reinforcement Learning
Embodied Learning and Intelligence for eXploration and Innovative soft Robotics (ELIXIR) Lab, Toronto Metropolitan University, Toronto, ON, Canada（体现学习与智能探索与创新软机器人（ELIXIR）实验室，多伦多 Metropolitan 大学，多伦多，ON，加拿大）
Learning Whole-Body Loco-Manipulation for Omni-Directional Task Space Pose Tracking with a Wheeled-Quadrupedal-Manipulator
School of System Design and Intelligent Manufacturing (SDIM), Southern University of Science and Technology, Shenzhen, China（系统设计与智能制造学院，南方科技大学，深圳，中国）；Zhejiang University-University of Illinois Urbana-Champaign Institute (ZJUI), Haining, China（浙江大学-伊利诺伊大学厄巴纳-香槟分校联合学院，海宁，中国）；LimX Dynamics, Shenzhen, China（LimX Dynamics，深圳，中国）
Learning Smooth and Robust Space Robotic Manipulation of Dynamic Target via Inter-frame Correlation
Research Center for Intelligent Robotics, School of Astronautics, Northwestern Polytechnical University（西北工业大学航天学院智能机器人研究中心）；School of Automation, Northwestern Polytechnical University（西北工业大学自动化学院）；School of Aeronautics and Astronautics, Sichuan University（四川大学空天科学与工程学院）
