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Unknown surface material classification (SMC) can inform a robot about material properties, enabling it to interact with environments appropriately. Recent research has leveraged multimodal data using deep learning to improve the performance of SMC. In this article, we present a deep learning model, multimodal temporal convolutional neural network (MTCNN), which integrates energy spectrum, dilated convolutions, and sequence poolings into a unified network architecture. The proposed model can learn material representations from auditory and multitactile (i.e., acceleration, normal force, and friction force) data generated by dragging a tool along surfaces, and distinguish unknown object surface materials into categories. For surface material data collection, a tool is also designed to detect different object surfaces. The performance of MTCNN is evaluated on a public dataset and the highest classification accuracy is 87.55%. A robotic curling example is provided to illustrate how the presented model helps the robot in manipulation. [ABSTRACT FROM AUTHOR]

Next‐generation robots are being designed to function autonomously in complex and unstructured environments. In particular, based on the real‐time measurement and differentiation of normal pressure and shear force, robots can be equipped with the capabilities of damage‐free grasp within minimum force limits, as well as dexterous operation through surface roughness and slip information. Herein, a flexible tactile sensor with a small cylinder protrusion and four arc‐shaped protrusions is developed. Due to its center symmetry and axisymmetry characteristics, the normal pressure and shear force can be decoupled from the complex stress without any interference from torsion. The flexible tactile sensor exhibits good linearity and superior cycling stability and is capable of determining the magnitude and direction of the applied force accurately. The flexible tactile sensor is comfortable to wear, and it is integrated onto the manipulator to realize various delicate and dexterous tasks, such as pressure detection, interaction with fragile objects, and roughness identification. Moreover, intelligent recognition of the sliding and stationary states can be achieved by decoding signals of sliding friction and static friction from the feedback information, leading to real time and precise adjustment of the grasping state of the manipulator. [ABSTRACT FROM AUTHOR]

Common ragweed (Ambrosia artemisiifolia) is a notorious invasive weed that has spread across most temperate regions of the world. The beetle (Ophraella communa) is considered to be an effective control agent against A. artemisiifolia. As an oligophagous insect, its olfactory system is extremely important for host seeking in the wild. To the best of our knowledge, there is no report on the molecular mechanisms underlying olfaction recognition in this beetle. Hence, in this study, we characterized the odorant receptor co‐receptor of O. communa and named it as 'OcomORco'. Real‐time quantitative PCR (qRT‐PCR) showed that, compared to the control treatment, RNA interference (RNAi) strongly reduced the expression of OcomORco by 89% in male and 90% in female beetles. Electroantennogram assay showed that the antennal response of both male and female beetles to four volatiles of A. artemisiifolia was significantly reduced. The injected male or female beetles lost their preference for plant leaves as observed in the behavioural tests. In addition, disruption of the expression of OcomORco resulted in a reduction of oviposition, while there was no difference in larval hatching rate between control and knockdown females. We demonstrated that OcomORco plays a vital role in olfactory perception and host search in O. communa, and it is involved in oviposition in an indirect way. [ABSTRACT FROM AUTHOR]

Increase in atmospheric CO2 directly affects the insect physiology and behavior, and indirectly affects the herbivorous insects by affecting their hosts. The increase in atmospheric CO2 is accompanied by an increase in temperature and heat waves. Ophraella communa LeSage is a natural enemy of Ambrosia artemisiifolia (common ragweed). The development and reproduction of this beetle is weakened upon eating common ragweed grown under stress conditions. As female behavior and physiology alter after mating, the reproductive tract of males is likely to modulate reproduction and development in this species. Herein, the transcriptional profiles of testes and accessory glands from male O. communa individuals feeding on common ragweed under conditions of high CO2 concentration and heat waves and that grown under ambient CO2 concentration were compared. Differentially expressed genes (DEGs) were identified between the same tissues from beetles fed on common ragweed grown under different stress conditions. There were 3, 2, 3, 1and 5 genes related to decomposition and transport of macromolecular substances, host location, stress response, reproduction, and poisonous food-utilization. No expected response was observed in the male reproductive tract, but some of the identified DEGs might control the development of the population. The results presented here should be helpful in guiding future studies on deciphering the indirect response of other organs to high CO2 concentration and heat waves, as well as the functions of seminal fluid proteins in O. communa. [ABSTRACT FROM AUTHOR]