The field of robotic manipulation and teleoperation is rapidly advancing, with a focus on developing more dexterous and intuitive control systems. Researchers are exploring new methods for learning robotic hand control, including the use of sensorized exoskeletons and simulation-based dynamics filters. Multisensory touch representations and vision-based tactile sensors are also being developed to improve tactile perception and object manipulation. Furthermore, novel models and approaches are being proposed for compliance detection, force feedback, and aerial grasping. These advancements have the potential to unlock a wider range of manipulation skills and enhance the capabilities of teleoperated robots. Noteworthy papers in this area include ExoStart, which presents a general and scalable learning framework for robotic hand control, and Sparsh-X, which introduces a multisensory touch representation for robot manipulation. Additionally, the paper on A Force Feedback Exoskeleton for Teleoperation Using Magnetorheological Clutches proposes a semi-active force feedback strategy based on MR clutches, while Aerial Grasping via Maximizing Delta-Arm Workspace Utilization introduces a novel planning framework for aerial grasping that maximizes workspace utilization.