High-resolution tactile perception is essential for humanoid robots to perform contact-based interaction tasks. However, enhancing resolution is typically accompanied by increasing the density of sensing nodes, large numbers of interconnecting wires, and complex signal processing modules. This work presents super-resolution (SR) tactile sensor arrays with sparsely distributed taxels powered by a universal intelligent framework. Such smart sensor systems involve a general topological optimization strategy for taxel layout design and a deep learning model called self-attention–assisted tactile SR. Driven by the proposed model, they can dynamically distinguish high-density pressure stimuli by generating 2700 virtual taxels from only 23 physical taxels. An SR scale factor of more than 115 and an average localization error of 0.73 millimeters are achieved, approximating human fingertip accuracy and surpassing current state-of-the-art solutions. This framework enhances flexible sensors with SR capabilities in a facile and energy-efficient manner, illustrating the potential to equip robots with embodied tactile perceptions.

Robot skin, as the physical interface between collaborative robots (cobots) and the external environments, could mediate on-body tactile signals for cobots to respond timely once a collision occurs during tasks and thus avoid damages to both cobots and surroundings. To achieve effective on-body tactile perception, robot skin should possess adequate conformal adaptability to be covered on various complex contours on the cobot, such as joints, while maintaining accurate and rapid sensing performance. In this letter, a soft robot skin consisting of an array of force-sensing units and a substrate with a serpentine structure was developed, both of which were made of porous material. The serpentine structure endows the developed skin with stretchable attributes. Thus, the skin could be conformally attached to complex surfaces of a cobot with less local stress, which was validated by finite element analysis. Taking the advantage of porous microstructure, the sensing units embedded in the substrate have exhibited high sensitivity and fast response time under external force, which are essential factors in real-time collision detection. Finally, experimental validation was carried out to verify the feasibility of deploying the developed soft robot skin on a robot arm to provide on-body tactile perception for a cobot.

As many robot applications become more reliant on wireless communications, wireless network latency and reliability have a growing impact on robot control. This article proposes a network hardware-in-the-loop (N-HiL) simulation framework to evaluate the impacts of wireless on robot control more efficiently and accurately, and then improve the design by employing correlation analysis between communication and control performances. The N-HiL method provides communication and robot developers with more trustworthy network conditions, while the huge efforts and costs of building and testing the entire physical robot system in real life are eliminated. These benefits are showcased in two representative latency-sensitive applications: 1) safe multirobot coordination for mobile robots, and 2) human-motion-based teleoperation for manipulators. Moreover, we deliver a preliminary assessment of two new-generation wireless technologies, the Wi-Fi6 and 5G, for those applications, which has demonstrated the effectiveness of the N-HiL method as well as the attractiveness of the wireless technologies.

Driven by the demand to largely mitigate nosocomial infection problems in combating the coronavirus disease 2019 (COVID-19) pandemic, the trend of developing technologies for teleoperation of medical assistive robots is emerging. However, traditional teleoperation of robots requires professional training and sophisticated manipulation, imposing a burden on healthcare workers, taking a long time to deploy, and conflicting the urgent demand for a timely and effective response to the pandemic. This paper presents a novel motion synchronization method enabled by the hybrid mapping technique of hand gesture and upper-limb motion (GuLiM). It tackles a limitation that the existing motion mapping scheme has to be customized according to the kinematic configuration of operators. The operator awakes the robot from any initial pose state without extra calibration procedure, thereby reducing operational complexity and relieving unnecessary pre-training, making it user-friendly for healthcare workers to master teleoperation skills. Experimenting with robotic grasping tasks verifies the outperformance of the proposed GuLiM method compared with the traditional direct mapping method. Moreover, a field investigation of GuLiM illustrates its potential for the teleoperation of medical assistive robots in the isolation ward as the Second Body of healthcare workers for telehealthcare, avoiding exposure of healthcare workers to the COVID-19.

With the fourth revolution of healthcare, i.e., Healthcare 4.0, collaborative robotics is spilling out from traditional manufacturing and will blend into human living or working environments to deliver care services, especially telehealthcare. Because of the frequent and seamless interaction between robots and care recipients, it poses several challenges that require careful consideration: 1) the ability of the human to collaborate with the robots in a natural manner; and 2) the safety of the human collaborating with the robot. In this regard, we have proposed a proximity sensing solution based on the self-capacitive technology to provide an extended sense of touch for collaborative robots, allowing approach and contact measurement to enhance safe and natural human-robot collaboration. The modular design of our solution enables it to scale up to form a large-area sensing system. The sensing solution is proposed to work in two operation modes: the interaction mode and the safety mode. In the interaction mode, utilizing the ability of the sensor to localize the point of action, gesture command is used for robot manipulation. In the safety mode, the sensor enables the robot to actively avoid obstacles.