The field of robotic hands is moving towards more advanced and adaptive designs, with a focus on dexterous teleoperation and soft robotic hands. Researchers are exploring new materials, mechanisms, and control strategies to improve the functionality and responsiveness of robotic hands. Notably, the development of affordable and fully-actuated biomimetic hands is gaining traction, with applications in areas such as prosthetics and underwater robotics. Some noteworthy papers include: The paper on the Educational SoftHand-A presents a novel design for an anthropomorphic robot hand built using LEGO MINDSTORMS, showcasing a simple and adaptive grasping mechanism. The paper on Learning to Design Soft Hands using Reward Models proposes a framework for optimizing soft robotic hands using a Cross-Entropy Method with Reward Model, demonstrating significant improvements in grasping success rates. The paper on the RAPID Hand Prototype introduces a low-cost, fully-actuated dexterous hand with a novel anthropomorphic actuation and transmission scheme, showing promise for dexterous teleoperation. The paper on A Learning-based Model Reference Adaptive Controller Implemented on a Prosthetic Hand Wrist presents a computationally efficient Neural Network-based Model Reference Adaptive Controller for a tendon-driven soft continuum wrist, enhancing motion accuracy and responsiveness in soft prosthetic systems. The paper on Optimizing Prosthetic Wrist Movement: A Model Predictive Control Approach presents an implementation of a Model Predictive Control strategy to regulate the motions of a soft continuum wrist section, enabling precise movement adjustments and improving prosthetic hand dexterity. The paper on MR-UBi: Mixed Reality-Based Underwater Robot Arm Teleoperation System with Reaction Torque Indicator via Bilateral Control presents a mixed reality-based underwater robot arm teleoperation system, significantly improving grasping-torque control accuracy and user experience.