The field of legged robotics is moving towards the development of more robust, efficient, and adaptable control systems. Researchers are exploring new approaches to locomotion planning, control, and damage recovery, with a focus on enabling legged robots to operate effectively in complex and dynamic environments. Notable trends include the integration of mechanical intelligence and computational intelligence to achieve robustness and reliability, as well as the use of machine learning and optimization techniques to improve performance and efficiency. Some papers are particularly noteworthy, including one that presents a novel paradigm for constructing robust and simply controlled multi-legged elongate robots, and another that evaluates Lagrangian Neural Networks for infinite horizon planning in quadrupedal locomotion, demonstrating significant improvements in sample efficiency and prediction accuracy. Other notable papers include one that proposes a hierarchical reinforcement learning framework for quadruped locomotion over challenging terrain, and another that presents a self-modeling and damage identification algorithm for autonomous adaptation to partial or complete leg loss in multi-legged robots.