The field of autonomous systems and planning is witnessing significant developments, with a focus on enhancing the capabilities of robots and agents to operate effectively in complex environments. Recent research has explored the use of learning-based approaches, such as neural networks and graph neural networks, to improve the efficiency and adaptability of planning algorithms.

Notably, innovations in areas like swarming, persistent monitoring, and target detection have led to the development of more robust and resilient systems. The integration of techniques like model predictive control, behavior trees, and pseudo-Boolean proof logging has also improved the performance and reliability of autonomous systems.

Furthermore, advances in heuristic search and planning have enabled the development of more efficient and optimal solutions for complex problems. The use of dynamic heuristics, finite-state controllers, and weighted-scenario optimization has expanded the capabilities of planning algorithms, allowing them to handle uncertainty and stochasticity more effectively.

Some noteworthy papers in this area include:

• Learning Attentive Neural Processes for Planning with Pushing Actions, which proposes a novel approach to planning with unknown physical properties.
• Aerial Robots Persistent Monitoring and Target Detection, which presents a distributed algorithm for multi-robot persistent monitoring and target detection.
• Leveraging Action Relational Structures for Integrated Learning and Planning, which introduces a new search space for classical planning that leverages the relational structure of actions.