This paper presents a Spatiotemporal Tube (STT)-based control framework for general unknown nonlinear Euler-Lagrange (EL) systems subject to input constraints, with the objective of satisfying Signal Temporal Logic (STL) specifications, where confinement of the system trajectory within the STT guarantees the satisfaction of the corresponding STL task. For both single and multi-agent scenarios, the STT corresponding to each agent is modeled as a time-varying ball, whose center and radius are jointly parameterized using a physics-informed neural network (PINN). The robustness metric associated with the STL specification corresponding to the agents is incorporated into the training process as a loss function, enabling the learned tube to encode task-level temporal requirements. For a multi-agent scenario, we introduce an additional robustness metric corresponding to the global task, which, when satisfied, ensures the tubes do not collide with each other. To ensure that the system trajectory remains within the learned STT and thereby satisfies the local and global STL specifications, we propose a control strategy that explicitly accounts for input constraints. In particular, a closed-form control law is developed to keep the trajectory inside the tube while regulating the motion of the tube by enforcing bounds on its evolution depending on the input constraints of the system. The proposed approach has been validated over several case studies.
STL-SVPIO: Signal Temporal Logic guided Stein Variational Path Integral Optimization
Department of Electrical and Systems Engineering, University of Pennsylvania（宾夕法尼亚大学电气与系统工程系）；Mechanical Engineering and Mechanics Department, Lehigh University（莱特大学机械工程与力学系）
BT-TL-DMPs: A Novel Robot TAMP Framework Combining Behavior Tree, Temporal Logic and Dynamical Movement Primitives
Institute of Robotics and Automatic Information Systems, College of Artificial Intelligence, Nankai University（机器人与自动信息系统研究所，人工智能学院，南开大学）；Tianjin Key Laboratory of Intelligent Robotics, Nankai University（智能机器人重点实验室，南开大学）；College of Artificial Intelligence, Nankai University（人工智能学院，南开大学）；Department of Computing, The Hong Kong Polytechnic University（计算学院，香港理工大学）
Trajectory Planning with Signal Temporal Logic Costs using Deterministic Path Integral Optimization
ZF Friedrichshafen AG（采埃孚腓德烈斯哈芬股份公司）；HTWG Konstanz（康斯坦茨应用科学大学）；School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）
本文提出一种基于 model predictive path integral control 的采样方法，用于高效求解带有 Signal temporal logic (STL) 成本函数的最优控制与轨迹规划问题。
LLM-Enabled Low-Altitude UAV Natural Language Navigation via Signal Temporal Logic Specification Translation and Repair
Guangdong Provincial Key Laboratory of Space-Aerial Networking and Intelligent Sensing, Harbin Institute of Technology, Shenzhen（广东省空天网络与智能感知重点实验室，哈尔滨工业大学（深圳））；Peng Cheng Laboratory (PCL), Shenzhen（鹏城实验室）；Information Systems Technology and Design, Singapore University of Technology and Design（新加坡科技设计大学信息系统技术与设计系）；School of Computer Science and Engineering, Central South University, Changsha（中南大学计算机科学与工程学院）
