This paper presents a textile-based wearable human-machine interface that integrates tactile sensors and vibrotactile haptic actuators, designed and fabricated using digital embroidery. The interface aims to capture, convey, and share tactile information across time and space, with potential applications in healthcare, augmented and virtual reality, human-robot collaboration, and skill development. The smart gloves are customizable, scalable, and modular, allowing for rapid fabrication within 10 minutes using low-cost materials. The vibrotactile haptics are based on linear resonant actuator structures, while the tactile sensors are piezoresistive. User studies demonstrate the effectiveness of the interface in perceiving haptic feedback and transferring physical interactions. An adaptive machine-learning pipeline optimizes haptic feedback for individual users, eliminating the need for manual calibration. The system is evaluated in various contexts, including alleviating tactile occlusion, guiding physical skill development, and enabling teleoperation of robots. The results show that the interface can effectively transfer tactile interactions, improve task performance, and enhance teleoperation performance in challenging scenarios.This paper presents a textile-based wearable human-machine interface that integrates tactile sensors and vibrotactile haptic actuators, designed and fabricated using digital embroidery. The interface aims to capture, convey, and share tactile information across time and space, with potential applications in healthcare, augmented and virtual reality, human-robot collaboration, and skill development. The smart gloves are customizable, scalable, and modular, allowing for rapid fabrication within 10 minutes using low-cost materials. The vibrotactile haptics are based on linear resonant actuator structures, while the tactile sensors are piezoresistive. User studies demonstrate the effectiveness of the interface in perceiving haptic feedback and transferring physical interactions. An adaptive machine-learning pipeline optimizes haptic feedback for individual users, eliminating the need for manual calibration. The system is evaluated in various contexts, including alleviating tactile occlusion, guiding physical skill development, and enabling teleoperation of robots. The results show that the interface can effectively transfer tactile interactions, improve task performance, and enhance teleoperation performance in challenging scenarios.