Although manipulation capabilities of aerial robots greatly improved in the last decade, only few works addressed the problem of aerial physical interaction with dynamic environments, proposing strongly model-based approaches. However, in real scenarios, modeling the environment with high accuracy is often impossible. In this work we aim at developing a control framework for OMAVs for reliable physical interaction tasks with articulated and movable objects in the presence of possibly unforeseen disturbances, and without relying on an accurate model of the environment. Inspired by previous applications of energy-based controllers for physical interaction, we propose a passivity-based impedance and wrench tracking controller in combination with a momentum-based wrench estimator. This is combined with an energy-tank framework to guarantee the stability of the system, while energy and power flow-based adaptation policies are deployed to enable safe interaction with any type of passive environment. The control framework provides formal guarantees of stability, which is validated in practice considering the challenging task of pushing a cart of unknown mass, moving on a surface of unknown friction, as well as subjected to unknown disturbances. For this scenario, we present, evaluate and discuss three different policies.
Precise Aggressive Aerial Maneuvers with Sensorimotor Policies
Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院工业控制技术研究所）；Huzhou Institute of Zhejiang University（浙江大学湖州研究院）；Differential Robotics Technology Co., Ltd.（微分机器人技术有限公司）
School of Automation Science and Engineering, South China University of Technology（华南理工大学自动化科学与工程学院）；State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所机器人重点实验室）；State Key Laboratory of Robotics and System, School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）机械工程与自动化学院机器人与系统重点实验室）
Separation Assurance between Heterogeneous Fleets of Small Unmanned Aerial Systems via Multi-Agent Reinforcement Learning
Department of Mechanical and Aerospace Engineering, George Washington University（机械与航空航天工程系，乔治华盛顿大学）；Department of Electrical and Computer Engineering, Purdue University（电气与计算机工程系，普渡大学）
Enhancing Aerial Combat Tactics through Hierarchical Multi-Agent Reinforcement Learning
Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA), USI-SUPSI（瑞士智能系统研究所（IDSIA）及USI-SUPSI）；Armasuisse Science + Technology（Armasuisse科学与技术）
GaussFly: Contrastive Reinforcement Learning for Visuomotor Policies in 3D Gaussian Fields
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）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；College of Design and Engineering, National University of Singapore（新加坡国立大学设计与工程学院）；College of Automation Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学自动化学院）
Fly-by-Code: Embodied Coding Agents for Aerial Manipulation with Active Visual and Physical Feedback
Seoul National University（首尔国立大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Maryland, College Park（马里兰大学帕克分校）；All Purpose AI；Kyung Hee University（庆熙大学）
Self-Predictive Representation for Autonomous UAV Object-Goal Navigation
Escola Politécnica de Pernambuco, Universidade de Pernambuco（巴伊兰联邦大学皮奥伊技术学院）；School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）；School of Mechanical and Manufacturing Engineering, University of New South Wales（新南威尔士大学机械与制造工程学院）；Escuela de Ingeniería, Universidad Central de Chile（智利中央大学工程学院）
