The field of robotics is witnessing significant developments in manipulation and mobility, with a focus on adaptability, efficiency, and robustness. Researchers are exploring innovative mechanisms, such as underactuated metamorphic loading manipulators, which can reconfigure their topology to grasp diverse objects in dynamic environments. Adaptive dual-arm manipulation strategies are also being developed to handle complex tasks like bagging and object manipulation. Furthermore, advances in computer vision and machine learning are enabling robots to learn from demonstrations and adapt to new situations, such as picking strawberries in cluttered environments. Noteworthy papers in this area include: The paper on Kinetostatics and Particle-Swarm Optimization of Vehicle-Mounted Underactuated Metamorphic Loading Manipulators, which proposes an innovative mechanism for efficient and adaptable loading solutions. The paper on Geometric Red-Teaming for Robotic Manipulation, which introduces a red-teaming framework to evaluate the robustness of robotic manipulation policies. The paper on Bio-inspired tail oscillation enables robot fast crawling on deformable granular terrains, which presents a bio-inspired approach to enhance robot locomotion on challenging substrates. The paper on M4Diffuser: Multi-View Diffusion Policy with Manipulability-Aware Control for Robust Mobile Manipulation, which proposes a hybrid framework for robust mobile manipulation in unstructured environments.