The field of autonomous systems and planning is moving towards more robust and efficient methods for decision-making and trajectory optimization. Recent research has focused on the development of novel planning frameworks that incorporate logical inference and proof theory, allowing for guaranteed logical validity and safety in autonomous systems. Additionally, there has been a surge in the application of convex optimization and sampling-based methods for trajectory planning in complex environments, such as urban areas and post-disaster scenarios. These methods have shown significant improvements in efficiency, scalability, and reliability compared to traditional approaches. Noteworthy papers in this area include:

• Constructive Symbolic Reinforcement Learning via Intuitionistic Logic and Goal-Chaining Inference, which introduces a novel learning and planning framework that replaces traditional reward-based optimization with constructive logical inference.
• Sampling-Based Planning Under STL Specifications: A Forward Invariance Approach, which proposes a variant of the RRT algorithm to synthesize trajectories satisfying a given spatio-temporal specification expressed in Signal Temporal Logic.