The field of soft robotics is moving towards the development of more advanced tactile sensing and grasping capabilities. Researchers are exploring new materials and manufacturing techniques to create soft and flexible robotic hands that can safely interact with delicate objects and environments. Innovations in continual learning and adaptive algorithms are enabling soft robotic systems to better adapt to changing conditions and improve their performance over time. Noteworthy papers include: Adaptive Drift Compensation for Soft Sensorized Finger Using Continual Learning, which introduces a novel approach to modeling and compensating for signal drift in soft robotic fingers. Shear-based Grasp Control for Multi-fingered Underactuated Tactile Robotic Hands, which presents a new control scheme for grasping and manipulating delicate objects using tactile feedback. Pellet-based 3D Printing of Soft Thermoplastic Elastomeric Membranes for Soft Robotic Applications, which demonstrates the feasibility of 3D printing soft robots using soft thermoplastic elastomers and membranes.