Blimps are well suited to perform long-duration aerial tasks as they are energy efficient, relatively silent and safe. To address the blimp navigation and control task, in previous work we developed a hardware and software-in-the-loop framework and a PID-based controller for large blimps in the presence of wind disturbance. However, blimps have a deformable structure and their dynamics are inherently non-linear and time-delayed, making PID controllers difficult to tune. Thus, often resulting in large tracking errors. Moreover, the buoyancy of a blimp is constantly changing due to variations in ambient temperature and pressure. To address these issues, in this paper we present a learning-based framework based on deep residual reinforcement learning (DRRL), for the blimp control task. Within this framework, we first employ a PID controller to provide baseline performance. Subsequently, the DRRL agent learns to modify the PID decisions by interaction with the environment. We demonstrate in simulation that DRRL agent consistently improves the PID performance. Through rigorous simulation experiments, we show that the agent is robust to changes in wind speed and buoyancy. In real-world experiments, we demonstrate that the agent, trained only in simulation, is sufficiently robust to control an actual blimp in windy conditions. We openly provide the source code of our approach at https://github.com/ robot-perception-group/AutonomousBlimpDRL.
Autonomous Docking of Multi-Rotor UAVs on Blimps under the Influence of Wind Gusts
University of Stuttgart, Faculty of Aerospace Engineering and Geodesy, Institute of Flight Mechanics and Control (iFR), Flight Robotics and Perception Group (FRPG)（斯图加特大学航空航天工程与大地测量学院飞行力学与控制研究所飞行机器人与感知组）；Max Planck Institute for Intelligent Systems, Tübingen, Perceiving Systems Department（马克斯·普朗克智能系统研究所图宾根感知系统部门）
Residual Chain Prediction for Autonomous Driving Path Planning
Technical University of Munich（慕尼黑工业大学）；School of Computer Science, Nanjing University of Information Science & Technology（南京信息工程大学计算机学院）；School of Computer and Information Engineering, Fuyang Normal University（阜阳师范大学计算机与信息工程学院）
针对端到端自动驾驶路径规划中行为克隆因曼哈顿距离产生协变量偏移的问题，提出 Residual Chain Loss 动态调整损失以增强时序依赖，并在 nuScenes 上验证其性能提升。
ForceRFT: Refining VLA Actions through Force-Guided Residual Reinforcement Learning
Eastern Institute of Technology（东方理工大学）；Jiangnan University（江南大学）；Shanghai Jiao Tong University（上海交通大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；National University of Singapore（新加坡国立大学）
RaceMOP: Mapless Online Path Planning for Multi-Agent Autonomous Racing using Residual Policy Learning
TUM School of Engineering and Design, Technical University of Munich（慕尼黑工业大学工程与设计学院）；Graduate School of Information Science and Technology, The University of Tokyo（东京大学信息科学与技术研究生院）
Adaptive Legged Locomotion via Online Learning for Model Predictive Control
Department of Aerospace Engineering, University of Michigan（密歇根大学航空航天工程系）；Institute for Robotics and Intelligent Machines, Georgia Institute of Technology（佐治亚理工学院机器人与智能机器研究所）
Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving
School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航空航天工程学院）；OpenDriveLab, the School of Computing and Data Science, The University of Hong Kong（开放驾驶实验室，计算与数据科学学院，香港大学）；Shanghai Innovation Institute（上海创新研究院）
Residual Cross-Modal Fusion Networks for Audio-Visual Navigation
School of Computer Science and Technology, Xinjiang University, Urumqi, China（新疆大学计算机科学与技术学院）；Joint International Research Laboratory of Silk Road Multilingual Cognitive（丝绸之路多语认知联合国际实验室）；School of Mechatronic Engineering and Automation Shanghai University, Shanghai, China（上海大学机电工程与自动化学院）
On the Need for a Statistical Foundation in Scenario-Based Testing of Autonomous Vehicles
WMG, University of Warwick（沃里克大学工学院）；Department of Computer Science, Chalmers University of Technology & University of Gothenburg（查尔姆斯理工大学计算机科学系）；Centre for Software Reliability, City St George’s, University of London（伦敦大学城市圣乔治学院软件可靠性中心）
VisionSafeEnhanced VPC: Cautious Predictive Control with Visibility Constraints under Uncertainty for Autonomous Robotic Surgery
School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；School of Advanced Manufacturing and Robotics, Peking University（北京大学先进制造与机器人学院）；MicroPort MedBot (Group) Company Ltd.（微宏医疗机器人（集团）有限公司）；Department of Biomedical Engineering, City University of Hong Kong（香港城市大学生物医学工程系）
Learning Autonomous Surgical Irrigation and Suction with the da Vinci Research Kit Using Reinforcement Learning
Department of Electrical and Computer Engineering, University of Alberta（电气与计算机工程系，阿尔伯塔大学）；Department of Biomedical Engineering, University of Alberta（生物医学工程系，阿尔伯塔大学）
