Advancement in human-robot interaction (HRI) is essential for the development of intelligent robots, but there lack paradigms to integrate remote control and tactile sensing for an ideal HRI. In this study, inspired b...
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Advancement in human-robot interaction (HRI) is essential for the development of intelligent robots, but there lack paradigms to integrate remote control and tactile sensing for an ideal HRI. In this study, inspired by the platypus beak sense, we propose a bionic electro-mechanosensory finger (EM-Finger) synergizing triboelectric and visuotactile sensing for remote control and tactile perception. A triboelectric sensor array made of a patterned liquid-metal-polymer conductive (LMPC) layer encodes both touchless and tactile interactions with external objects into voltage signals in the air, and responds to electrical stimuli underwater for amphibious wireless communication. Besides, a three-dimensional finger-shaped visuotactile sensing system with the same LMPC layer as a reflector measures contact-induced deformation through marker detection and tracking methods. A bioinspired bimodal deep learning algorithm implements data fusion of triboelectric and visuotactile signals and achieves the classification of 18 common material types under varying contact forces with an accuracy of 94.4 %. The amphibious wireless communication capability of the triboelectric sensor array enables touchless HRI in the air and underwater, even in the presence of obstacles, while the whole system realizes high resolution tactile sensing. By naturally integrating remote contorl and tactile sensing, the proposed EM-Finger could pave the way for enhanced HRI in machine intelligence.
Electronic skin with tactile perception enables intelligent robots and prostheses to perform dexterous manipulation and natural interaction with the human and surroundings. However, using single tactile sensing mechan...
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Electronic skin with tactile perception enables intelligent robots and prostheses to perform dexterous manipulation and natural interaction with the human and surroundings. However, using single tactile sensing mechanism to simultaneously percept geometry features and materials properties remains a challenge due to the bottleneck of signal decoupling. Herein, we report the MTSensing system - a wireless and fully-integrated tactile sensing system that can simultaneously recognize materials and textures based on a single flexible triboelectric sensor. The proposed triboelectric sensor converts touch into electrical signals and meanwhile, the signal processing pipeline decouples the signals into macro/micro features and feeds them into the corresponding deep learning models, which simultaneously predict the materials and textures of the contacted objects with the accuracies of 99.07% and 99.32%, respectively. The systematic integration of MTSensing hopes to pave the way for deploying low-cost and scalable electronic skin with multi-functional perceptions.
Federated learning (FL) has attracted tremendous attentions in recent years due to its privacy-preserving measures and great potential in some distributed but privacy-sensitive applications, such as finance and health...
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Federated learning (FL) has attracted tremendous attentions in recent years due to its privacy-preserving measures and great potential in some distributed but privacy-sensitive applications, such as finance and health. However, high communication overloads for transmitting high-dimensional networks and extra security masks remain a bottleneck of FL. This article proposes a communication-efficient FL framework with an Adaptive Quantized Gradient (AQG), which adaptively adjusts the quantization level based on a local gradient's update to fully utilize the heterogeneity of local data distribution for reducing unnecessary transmissions. In addition, client dropout issues are taken into account and an Augmented AQG is developed, which could limit the dropout noise with an appropriate amplification mechanism for transmitted gradients. Theoretical analysis and experiment results show that the proposed AQG leads to 18% to 50% of additional transmission reduction as compared with existing popular methods, including Quantized Gradient Descent (QGD) and Lazily Aggregated Quantized (LAQ) gradient-based methods without deteriorating convergence properties. Experiments with heterogenous data distributions corroborate a more significant transmission reduction compared with independent identical data distributions. The proposed AQG is robust to a client dropping rate up to 90% empirically, and the Augmented AQG manages to further improve the FL system's communication efficiency with the presence of moderate-scale client dropouts commonly seen in practical FL scenarios.
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