The field of legged robotics is rapidly advancing, with a focus on developing more agile, robust, and adaptable systems. Recent research has highlighted the importance of integrating learning-based approaches with traditional control methods to achieve more effective and efficient locomotion and manipulation. One notable trend is the use of reinforcement learning and attention mechanisms to improve the robustness and adaptability of legged robots in complex environments. Additionally, there is a growing interest in developing more versatile and scalable control frameworks that can accommodate a wide range of tasks and scenarios. Noteworthy papers in this area include Learning to Recover: Dynamic Reward Shaping with Wheel-Leg Coordination for Fallen Robots, which presents a novel learning-based framework for adaptive recovery from fall incidents, and SkillBlender: Towards Versatile Humanoid Whole-Body Loco-Manipulation via Skill Blending, which introduces a hierarchical reinforcement learning framework for versatile humanoid loco-manipulation. Attention-Based Map Encoding for Learning Generalized Legged Locomotion is also noteworthy, as it proposes a novel approach to learning generalized legged locomotion using attention-based map encoding.