Deep Lagrangian Networks for end-to-end learning of energy-based control for under-actuated systems
Technische Universität Darmstadt（达姆施塔特工业大学）；ABB Corporate Research Center Germany（ABB德国企业研究中心）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Applying Deep Learning to control has a lot of potential for enabling the intelligent design of robot control laws. Unfortunately common deep learning approaches to control, such as deep reinforcement learning, require an unrealistic amount of interaction with the real system, do not yield any performance guarantees, and do not make good use of extensive insights from model-based control. In particular, common black-box approaches -- that abandon all insight from control -- are not suitable for complex robot systems. We propose a deep control approach as a bridge between the solid theoretical foundations of energy-based control and the flexibility of deep learning. To accomplish this goal, we extend Deep Lagrangian Networks (DeLaN) to not only adhere to Lagrangian Mechanics but also ensure conservation of energy and passivity of the learned representation. This novel extension is embedded within generic model-based control laws to enable energy control of under-actuated systems. The resulting DeLaN for energy control (DeLaN 4EC) is the first model learning approach using generic function approximation that is capable of learning energy control. DeLaN 4EC exhibits excellent real-time control on the physical Furuta Pendulum and learns to swing-up the pendulum while the control law using system identification does not.
Design, Dynamic Modeling and Control of a 2-DOF Robotic Wrist Actuated by Twisted and Coiled Actuators
College of Advanced Manufacturing and Robotics（先进制造与机器人学院）；State Key Laboratory of Turbulence and Complex Systems（湍流与复杂系统国家重点实验室）；Peking University（北京大学）；Department of Electrical and Computer Engineering（电气与计算机工程系）
Safe Multi-Agent Deep Reinforcement Learning for Privacy-Aware Edge-Device Collaborative DNN Inference
School of Future Science and Engineering, Soochow University（未来科学与工程学院，苏州大学）；College of Computer and Information Sciences, Fujian Agriculture and Forestry University（计算机与信息科学学院，福建农林大学）；School of Information and Control Engineering, China University of Mining and Technology（信息与控制工程学院，中国矿业大学）；Department of Control Science and Engineering, Tongji University（控制科学与工程学院，同济大学）；School of Computing Technologies, RMIT University（计算技术学院，拉筹伯大学）
Neural Operators for Design-Space Surrogate Modeling of Tendon-Actuated Continuum Robots
The Robotics Center and the Kahlert School of Computing at the University of Utah（犹他大学机器人中心和Kahlert计算学院）；The Departments of Computer Science and Electrical and Computer Engineering at Vanderbilt University（范德比大学计算机科学与电气与计算机工程系）
Generalized Momenta-Based Koopman Formalism for Robust Control of Euler-Lagrangian Systems
Department of Mechanical Engineering, Indian Institute of Science（机械工程系，印度科学研究院）；Robert Bosch Centre for Cyber-Physical Systems, Indian Institute of Science（网络物理系统研究中心，印度科学研究院）
Safe and Sustainable Electric Bus Charging Scheduling with Constrained Hierarchical DRL
College of Engineering, University of Guelph（圭尔夫大学工程学院）；EthicalAI；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
Technical University of Darmstadt（德累斯顿技术大学）；University of São Paulo（圣保罗大学）；Istituto Italiano di Tecnologia（意大利理工研究院）；German Research Center for AI（德国人工智能研究中心）；Robotics Institute Germany (RIG)（德国机器人研究所）
Context-Aware Deep Lagrangian Networks for Model Predictive Control
Department of Computer Science, Technical University of Darmstadt（达姆斯塔特技术大学计算机科学系）；German Research Center for AI (DFKI), Research Department: Systems AI for Robot Learning（德国人工智能研究中心（DFKI）系统AI机器人学习研究部）；Robotics Institute Germany (RIG)（德国机器人研究所）
