In the future, the combination of artificial intelligence (AI) technology with the IoT technology will lead to AI of things (AIoT)-based living, working, and manufacturing environment, which provides efficient IoT operations, improved human–machine interactions and better capability of decision-making with respect to a complicated and dynamic system 2, 3. Such infrastructure will enable the smart homes and lean/smart manufacturing by deploying huge amounts of sensors under the internet of things (IoTs) framework to realize real-time sensory information collection, data management and analysis 1. With the establishment of 5 G technology in the next few years, the cost of massive data transmission via wireless network will be much cheaper. Demonstration of digital twin applications, which show the object identification and duplicate robotic manipulation in virtual environment according to the real-time operation of the soft-robotic gripper system, is successfully created for virtual assembly lines and unmanned warehouse applications. The triboelectric sensory information collected during the operation of soft gripper is further trained by support vector machine algorithm to identify diverse objects with an accuracy of 98.1%. The gear-based length sensor with a stretchable strip allows the continuous detection of elongation via the sequential contact of each tooth. With the special distributed electrodes, the tactile sensor can perceive the contact position and area of external stimuli. Here, we report a smart soft-robotic gripper system based on triboelectric nanogenerator sensors to capture the continuous motion and tactile information for soft gripper. Designing efficient sensors for soft robotics aiming at human machine interaction remains a challenge.
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