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The manufacturing industry has witnessed advancements in soft robotics, specifically in robotic grippers for handling fragile or irregular objects. However, challenges remain in balancing shape compliance, structural rigidity, weight, and sensor integration. To address these limitations, a 3D‐printed multimaterial gripper design is proposed. This approach utilizes a single, nearly fully automated 3D printing process to create a universal gripper with almost no assembly work. By processing functional polymer, polymer nanocomposite, and metal wire simultaneously, this technique enables multifunctionality. The gripper achieves different gripping configurations by adjusting joint stiffness through Joule heating of conductive polylactic acid material, ensuring shape conformance. Embedded metal wires, created using an in‐house wire embedding technique, form reliable high‐current‐loading interconnections for the conductive joints acting as the heater. Additionally, an integrated soft sensor printed in thermoplastic polyurethane (TPU) and conductive TPU detects compression levels and discerns handled samples. This study showcases the potential of 3D multimaterial printing for on‐demand fabrication of a smart universal gripper with variable stiffness and integrated sensors, benefiting the automation industry. Overall, this work presents an effective strategy for designing and fabricating integrated multifunctional structures using soft, rigid, and conductive materials, such as polymer, polymer nanocomposite, and metal through multimaterial 3D printing. [ABSTRACT FROM AUTHOR]

Skin‐inspired sensors are all the rage in robotic applications. They take inspiration from the human skin's sensory abilities and use their abilities to sense things like temperature and pressure. Herein, fabrication of ultra‐low‐cost (<$1.5), ultra‐thin, wide range, and crosstalk‐free skin‐inspired tactile sensors is presented. The sensors consist of piezoresistive pressure sensing elements sandwiched between 3D printed silver nanoparticle electrodes on polyimide layers just like the epidermis, dermis, and hypodermis of human skin. The response time of individual sensing nodes is 4 ms which is faster than the response time of the human skin (30–50 ms). The sensors exhibit high sensitivity (1.35 kPa−1), low hysteresis (9.22%), and a wide pressure sensing range (5–600 kPa). The sensor arrays are assembled on the fingertips of a commercial glove to make a smart glove. By combining the sensor information and deep learning, the smart glove is used to identify sharp and blunt objects with a classification accuracy of 95.9% and the direction of applied pressure when touched by an object with a classification accuracy of 97.8%. Furthermore, the smart glove is used to generate pressure maps in real‐time while grabbing six different objects handled by humans in daily life. [ABSTRACT FROM AUTHOR]