The field of autonomous systems is moving towards more efficient and adaptive control methods, with a focus on optimization techniques that can handle complex trade-offs between competing objectives. Recent research has explored the use of reinforcement learning, Pareto analysis, and nonlinear control strategies to improve the performance of autonomous systems in various applications, including lunar landing, remote state estimation, and vertical take-off and landing. These innovative approaches have shown promising results in terms of improved accuracy, reduced communication costs, and enhanced adaptability. Noteworthy papers in this area include:

• A paper on optimal thrust to mass ratio requirement for maximizing payload mass of lunar landing mission, which proposes a novel outer-layer optimization approach to achieve a globally optimal solution.
• A paper on the value of communication in goal-oriented semantic communications, which introduces an efficient and provably optimal algorithm for constructing the complete Pareto frontier.
• A paper on a learning-based control methodology for transitioning VTOL UAVs, which demonstrates a novel coupled transition control methodology based on reinforcement learning.
• A paper on vertical planetary landing on sloped terrain using optical flow divergence estimates, which proposes a nonlinear control strategy that leverages local flow divergence estimates to regulate thrust and attitude during landing.