Path smoothing and trajectory planning are universally applied in computer-numerical-
control (CNC) machining to avoid natural discontinuity of tangency and curvature at the …

To solve safety-critical decision-making problems, Inverse Constrained Reinforcement
Learning (ICRL) infers constraints from expert demonstrations and seeks to imitate expert …

Time-optimal control for high-order chain-of-integrator systems with full state constraints
remains an open and challenging problem within the discipline of optimal control. The …

Reinforcement Learning (RL) has become a critical tool for optimization challenges within
automation, leading to significant advancements in several areas. This review article …

Collision-free robot motion planning is crucial in robotic applications. Traditional sampling-
based methods struggle with kinematic/dynamic constraints and intermediate process …

Multi-agent reinforcement learning (MARL) has achieved notable success in cooperative
tasks, demonstrating impressive performance and scalability. However, deploying MARL …

Most existing necessary conditions for optimal control based on adjoining methods require
both state and costate information, yet the unobservability of costates for a given feasible …