6-D Object Pose Estimation Based on Point Pair Matching for Robotic Grasp Detection.

The 6-D pose estimation is a critical work essential to achieve reliable robotic grasping. Currently, the prevalent method is reliant on keypoint correspondence. However, this approach hinges on the determination of object keypoint locations, alongside their detection and localization in real scenes. It also employs the random sample consensus (RANSAC)-based perspective-n-point (PnP) algorithm to solve the pose. Yet, it is nondifferentiable and incapable of backpropagation with loss during the training phase. Alternatively, the direct regression method, while speedy and differentiable, falls short in terms of pose estimation performance, and thus needs enhancement. In view of these gaps, we investigate PPM6D, a new method for 6-D object pose estimation based on regression and point pair matching. Our methodology begins with a proposed cross-fusion module, designed to achieve the fusion and complementation of RGB features and point cloud features. Subsequently, an attention module adjusts the features of the object's 3-D model. Finally, we design a point pair matching module for effective matching of points and characteristics, resulting in an integral matching and fusion. PPM6D is extensively trained and tested utilizing benchmark datasets like LINEMOD, occlusion LINEMOD (LINEMOD-occ), YCB-Video, and T-LESS dataset. Experimental results prove that PPM6D can outperform many keypoint-based pose estimation methods, given its relatively rapid speed, thereby offering novel regression-based pose estimation ideas. When applied to real-world scenarios of object pose estimation tasks and grasp tasks of an actual Baxter robot, PPM6D demonstrates superior performance as compared to most alternatives.

Cooperative Online Learning for Multiagent System Control via Gaussian Processes With Event-Triggered Mechanism.

In the realm of the cooperative control of multiagent systems (MASs) with unknown dynamics, Gaussian process (GP) regression is widely used to infer the uncertainties due to its modeling flexibility of nonlinear functions and the existence of a theoretical prediction error bound. Online learning, which involves incorporating newly acquired training data into GP models, promises to improve control performance by enhancing predictions during the operation. Therefore, this article investigates the online cooperative learning algorithm for MAS control. Moreover, an event-triggered data selection mechanism, inspired by the analysis of a centralized event-trigger (CET), is introduced to reduce the model update frequency and enhance the data efficiency. With the proposed learning-based control, the practical convergence of the MAS is validated with guaranteed tracking performance via the Lyapunov theory. Furthermore, the exclusion of the Zeno behavior for individual agents is shown. Finally, the effectiveness of the proposed event-triggered online learning method is demonstrated in simulations.

An Efficient Robotic Pushing and Grasping Method in Cluttered Scene.

Pushing and grasping (PG) are crucial skills for intelligent robots. These skills enable robots to perform complex grasping tasks in various scenarios. These PG methods can be categorized into single-stage and multistage approaches. Single-stage methods are faster but less accurate, while multistage methods offer high accuracy at the expense of time efficiency. To address this issue, a novel end-to-end PG method called efficient PG network (EPGNet) is proposed in this article. EPGNet achieves both high accuracy and efficiency simultaneously. To optimize performance with fewer parameters, EfficientNet-B0 is used as the backbone of EPGNet. Additionally, a novel cross-fusion module is introduced to enhance network performance in robotic PG tasks. This module fuses and utilizes local and global features, aiding the network in handling objects of varying sizes in different scenes. EPGNet consists of two branches dedicated to predicting PG actions, respectively. Both branches are trained simultaneously within a Q-learning framework. Training data is collected through trial and error, involving the robot performing PG actions. To bridge the gap between simulation and reality, a unique PG dataset is proposed. Additionally, a YOLACT network is trained on the PG dataset to facilitate object detection and segmentation. A comprehensive set of experiments is conducted in simulated environments and real-world scenarios. The results demonstrate that EPGNet outperforms single-stage methods and offers competitive performance compared to multistage methods, all while utilizing fewer parameters. A video is available at https://youtu.be/HNKJjQH0MPc.

Improved 2-round collision attack on IoT hash standard ASCON-HASH.

Lightweight cryptography algorithms are a class of ciphers designed to protect data generated and transmitted by the Internet of Things. They typically have low requirements in terms of storage space and power consumption, and are well-suited for resource-limited application scenarios such as embedded systems, actuators, and sensors. The NIST-approved competition for lightweight cryptography aims to identify lightweight cryptographic algorithms that can serve as standards. Its objective is to enhance data security in various scenarios. Among the chosen standards for lightweight cryptography, ASCON has been selected. ASCON-HASH is a hash function within the ASCON family. This paper presents a detailed analysis of the differential characteristics of ASCON-HASH, utilizing the quadratic S-box. Additionally, we employ message modification techniques and ultimately demonstrate a non-practical collision attack on the 2-round ASCON-HASH, requiring a time complexity of 298 hash function calls.

Foxp3-mediated blockage of ryanodine receptor 2 underlies contact-based suppression by regulatory T cells.

The suppression mechanism of Tregs remains an intensely investigated topic. As our focus has shifted toward a model centered on indirect inhibition of DCs, a universally applicable effector mechanism controlled by the transcription factor forkhead box P3 (Foxp3) expression has not been found. Here, we report that Foxp3 blocked the transcription of ER Ca2+-release channel ryanodine receptor 2 (RyR2). Reduced RyR2 shut down basal Ca2+ oscillation in Tregs, which reduced m-calpain activities that are needed for T cells to disengage from DCs, suggesting a persistent blockage of DC antigen presentation. RyR2 deficiency rendered the CD4+ T cell pool immune suppressive and caused it to behave in the same manner as Foxp3+ Tregs in viral infection, asthma, hypersensitivity, colitis, and tumor development. In the absence of Foxp3, Ryr2-deficient CD4+ T cells rescued the systemic autoimmunity associated with scurfy mice. Therefore, Foxp3-mediated Ca2+ signaling inhibition may be a central effector mechanism of Treg immune suppression.

Category-Level 6-D Object Pose Estimation With Shape Deformation for Robotic Grasp Detection.

Category-level 6-D object pose estimation plays a crucial role in achieving reliable robotic grasp detection. However, the disparity between synthetic and real datasets hinders the direct transfer of models trained on synthetic data to real-world scenarios, leading to ineffective results. Additionally, creating large-scale real datasets is a time-consuming and labor-intensive task. To overcome these challenges, we propose CatDeform, a novel category-level object pose estimation network trained on synthetic data but capable of delivering good performance on real datasets. In our approach, we introduce a transformer-based fusion module that enables the network to leverage multiple sources of information and enhance prediction accuracy through feature fusion. To ensure proper deformation of the prior point cloud to align with scene objects, we propose a transformer-based attention module that deforms the prior point cloud from both geometric and feature perspectives. Building upon CatDeform, we design a two-branch network for supervised learning, bridging the gap between synthetic and real datasets and achieving high-precision pose estimation in real-world scenes using predominantly synthetic data supplemented with a small amount of real data. To minimize reliance on large-scale real datasets, we train the network in a self-supervised manner by estimating object poses in real scenes based on the synthetic dataset without manual annotation. We conduct training and testing on CAMERA25 and REAL275 datasets, and our experimental results demonstrate that the proposed method outperforms state-of-the-art (SOTA) techniques in both self-supervised and supervised training paradigms. Finally, we apply CatDeform to object pose estimation and robotic grasp experiments in real-world scenarios, showcasing a higher grasp success rate.

Copper-catalyzed aerobic oxidation of primary alcohols to carboxylic acids.

Here, the first copper-catalyzed aerobic oxidation of primary alcohols to carboxylic acids with TEMPO and KHSO4 as the co-catalysts has been developed. The reaction exhibits excellent substrate scope and functional group compatibility under mild conditions. Even the very sensitive chiral alcohols, chiral amino alcohols, and alcohol-containing steroid skeletons may be oxidized to afford the corresponding carboxylic acids or lactones without racemization.

A Novel Robotic Pushing and Grasping Method Based on Vision Transformer and Convolution.

Robotic grasping techniques have been widely studied in recent years. However, it is always a challenging problem for robots to grasp in cluttered scenes. In this issue, objects are placed close to each other, and there is no space around for the robot to place the gripper, making it difficult to find a suitable grasping position. To solve this problem, this article proposes to use the combination of pushing and grasping (PG) actions to help grasp pose detection and robot grasping. We propose a pushing-grasping combined grasping network (GN), PG method based on transformer and convolution (PGTC). For the pushing action, we propose a vision transformer (ViT)-based object position prediction network pushing transformer network (PTNet), which can well capture the global and temporal features and can better predict the position of objects after pushing. To perform the grasping detection, we propose a cross dense fusion network (CDFNet), which can make full use of the RGB image and depth image, and fuse and refine them several times. Compared with previous networks, CDFNet is able to detect the optimal grasping position more accurately. Finally, we use the network for both simulation and actual UR3 robot grasping experiments and achieve SOTA performance. Video and dataset are available at https://youtu.be/Q58YE-Cc250.

Discrete-Time Control Barrier Function: High-Order Case and Adaptive Case.

This article proposes the novel concepts of the high-order discrete-time control barrier function (CBF) and adaptive discrete-time CBF. The high-order discrete-time CBF is used to guarantee forward invariance of a safe set for discrete-time systems of high relative degree. An optimization problem is then established unifying high-order discrete-time CBFs with discrete-time control Lyapunov functions to yield a safe controller. To improve the feasibility of such optimization problems, the adaptive discrete-time CBF is designed, which can relax constraints on system control input through time-varying penalty functions. The effectiveness of the proposed methods in dealing with high relative degree constraints and improving feasibility is verified on the discrete-time system of a three-link manipulator.

Flexible PVDF-TrFE Nanocomposites with Ag-decorated BCZT Heterostructures for Piezoelectric Nanogenerator Applications.

Flexible piezoelectric nanogenerators are playing an important role in delivering power to next-generation wearable electronic devices due to their high-power density and potential to create self-powered sensors for the Internet of Things. Among the range of available piezoelectric materials, poly(vinylidene fluoride-trifluoroethylene) (PVDF-TrFE)-based piezoelectric composites exhibit significant potential for flexible piezoelectric nanogenerator applications. However, the high electric fields that are required for poling cannot be readily applied to polymer composites containing piezoelectric fillers due to the high permittivity contrast between the filler and matrix, which reduces the dielectric strength. In this paper, novel Ag-decorated BCZT heterostructures were synthesized via a photoreduction method, which were introduced at a low level (3 wt %) into the matrix of PVDF-TrFE to fabricate piezoelectric composite films. The effect of Ag nanoparticle loading content on the dielectric, ferroelectric, and piezoelectric properties was investigated in detail, where a maximum piezoelectric energy-harvesting figure of merit of 5.68 × 10-12 m2/N was obtained in a 0.04Ag-BCZT NWs/PVDF-TrFE composite film, where 0.04 represents the concentration of the AgNO3 solution. Modeling showed that an optimum performance was achieved by tailoring the fraction and distribution of the conductive silver nanoparticles to achieve a careful balance between generating electric field concentrations to increase the level of polarization, while not degrading the dielectric strength. This work therefore provides a strategy for the design and manufacture of highly polarized piezoelectric composite films for piezoelectric nanogenerator applications.

Improved Energy Density and Energy Efficiency of Poly(vinylidene difluoride) Nanocomposite Dielectrics Using 0.93Na0.5Bi0.5TiO3-0.07BaTiO3 Nanofibers.

In modern electronic and power systems, it is essential to develop advanced dielectric materials with high energy density. One-dimensional ferroelectric ceramic nanofibers were proved to be a feasible strategy for improving the permittivity and energy density of nanocomposites. In this paper, to overcome the high loss issue of Na0.5Bi0.5TiO3 (NBT), a kind of novel nanofibers 0.93Na0.5Bi0.5TiO3-0.07BaTiO3 (NBT-BT) with large aspect ratio are synthesized by the electrospinning method and used as the dielectric fillers in trilayer structured poly(vinylidene difluoride) (PVDF) nanocomposites for energy storage applications. The finite element analysis is performed to evaluate the electric field distribution in the nanocomposites. The results showed that the trilayer structured nanocomposite loaded with 6 wt % NBT-BT nanofibers in the middle layer achieved the highest discharge energy density of 19.21 J cm-3 at 527 kV mm-1, which is 87.2% higher than that of pure PVDF (10.26 J cm-3 at 420 kV mm-1). Owing to the contribution of the barrier effect and interface polarization between adjacent layers, the energy density of the trilayer structured nanocomposites is significantly higher than that of the single-layer nanocomposites. This work provides a kind of novel one-dimensional ceramic fillers for obviously improving the energy density of polymer-based dielectrics.

Electrically Controlled Aquatic Soft Actuators with Desynchronized Actuation and Light-Mediated Reciprocal Locomotion.

Soft-bodied aquatic invertebrates can overcome hydrodynamic resistance and display diverse locomotion modes in response to environmental cues. Exploring the dynamics of locomotion from bioinspired aquatic actuators will broaden the perspective of underwater manipulation of artificial systems in fluidic environments. Here, we report a multilayer soft actuator design based on a light-driven hydrogel and a laser-induced graphene (LIG) actuator, minimizing the effect of the time delay by a monolithic hydrogel-based system while maintaining shape-morphing functionality. Moreover, different time scales in the response of actuator materials enable a real-time desynchronization of energy inputs, holding great potential for applications requiring desynchronized stimulation. This hybrid design principle is ultimately demonstrated with a high-performance aquatic soft actuator possessing an underwater walking speed of 0.81 body length per minute at a relatively low power consumption of 3 W. When integrated with an optical sensor, the soft actuator can sense the variation in light intensity and achieve mediated reciprocal motion. Our proposed locomotion mechanism could inspire other multilayer soft actuators to achieve underwater functionalities at the same spatiotemporal scale. The underwater actuation platform could be used to study locomotion kinematics and control mechanisms that mimic the motion of soft-bodied aquatic organisms.

An MVMD-CCA Recognition Algorithm in SSVEP-Based BCI and Its Application in Robot Control.

This article proposes a novel recognition algorithm for the steady-state visual evoked potentials (SSVEP)-based brain-computer interface (BCI) system. By combining the advantages of multivariate variational mode decomposition (MVMD) and canonical correlation analysis (CCA), an MVMD-CCA algorithm is investigated to improve the detection ability of SSVEP electroencephalogram (EEG) signals. In comparison with the classical filter bank canonical correlation analysis (FBCCA), the nonlinear and non-stationary EEG signals are decomposed into a fixed number of sub-bands by MVMD, which can enhance the effect of SSVEP-related sub-bands. The experimental results show that MVMD-CCA can effectively reduce the influence of noise and EEG artifacts and improve the performance of SSVEP-based BCI. The offline experiments show that the average accuracies of MVMD-CCA in the training dataset and testing dataset are improved by 3.08% and 1.67%, respectively. In the SSVEP-based online robotic manipulator grasping experiment, the recognition accuracies of the four subjects are 92.5%, 93.33%, 90.83%, and 91.67%, respectively.

Arginine deficiency is involved in thrombocytopenia and immunosuppression in severe fever with thrombocytopenia syndrome.

Severe fever with thrombocytopenia syndrome (SFTS) caused by a recently identified bunyavirus, SFTSV, is an emerging infectious disease with extensive geographical distribution and high mortality. Progressive viral replication and severe thrombocytopenia are key features of SFTSV infection and fatal outcome, whereas the underlying mechanisms are unknown. We revealed arginine deficiency in SFTS cases by performing metabolomics analysis on two independent patient cohorts, suggesting that arginine metabolism by nitric oxide synthase and arginase is a key pathway in SFTSV infection and consequential death. Arginine deficiency was associated with decreased intraplatelet nitric oxide (Plt-NO) concentration, platelet activation, and thrombocytopenia. An expansion of arginase-expressing granulocytic myeloid-derived suppressor cells was observed, which was related to T cell CD3-ζ chain down-regulation and virus clearance disturbance, implicating a role of arginase activity and arginine depletion in the impaired anti-SFTSV T cell function. Moreover, a comprehensive measurement of arginine bioavailability, global arginine bioavailability ratio, was shown to be a good prognostic marker for fatal prediction in early infection. A randomized controlled trial demonstrated that arginine administration was correlated with enhanced Plt-NO concentration, suppressed platelet activation, and elevated CD3-ζ chain expression and eventually associated with an accelerated virus clearance and thrombocytopenia recovery. Together, our findings revealed the arginine catabolism pathway-associated regulation of platelet homeostasis and T cell dysregulation after SFTSV infection, which not only provided a functional mechanism underlying SFTS pathogenesis but also offered an alternative therapy choice for SFTS.

A Novel Switching-Based Control Framework for Improved Task Performance in Teleoperation System With Asymmetric Time-Varying Delays.

This paper addresses the adaptive control for task-space teleoperation systems with constrained predefined synchronization error, where a novel switched control framework is investigated. Based on multiple Lyapunov-Krasovskii functionals method, the stability of the resulting closed-loop system is established in the sense of state-independent input-to-output stability. Compared with previous work, the developed method can simultaneously handle the unknown kinematics/dynamics, asymmetric varying time delays, and prescribed performance control in a unified framework. It is shown that the developed controller can guarantee the prescribed transient-state and steady-state synchronization performances between the master and slave robots, which is demonstrated by the simulation study.

Multilateral Telecoordinated Control of Multiple Robots With Uncertain Kinematics.

This paper addresses the telecoordinated control of multiple robots in the simultaneous presence of asymmetric time-varying delays, nonpassive external forces, and uncertain kinematics/dynamics. To achieve the control objective, a neuroadaptive controller with utilizing prescribed performance control and switching control technique is developed, where the basic idea is to employ the concept of motion synchronization in each pair of master-slave robots and among all slave robots. By using the multiple Lyapunov-Krasovskii functionals method, the state-independent input-to-output practical stability of the closed-loop system is established. Compared with the previous approaches, the new design is straightforward and easier to implement and is applicable to a wider area. Simulation results on three pairs of three degrees-of-freedom robots confirm the theoretical findings.

The RNA-binding protein QKI5 regulates primary miR-124-1 processing via a distal RNA motif during erythropoiesis.

MicroRNA (miRNA) biogenesis is finely controlled by complex layers of post-transcriptional regulators, including RNA-binding proteins (RBPs). Here, we show that an RBP, QKI5, activates the processing of primary miR-124-1 (pri-124-1) during erythropoiesis. QKI5 recognizes a distal QKI response element and recruits Microprocessor through interaction with DGCR8. Furthermore, the recruited Microprocessor is brought to pri-124-1 stem loops by a spatial RNA-RNA interaction between two complementary sequences. Thus, mutations disrupting their base-pairing affect the strength of QKI5 activation. When erythropoiesis proceeds, the concomitant decrease of QKI5 releases Microprocessor from pri-124-1 and reduces mature miR-124 levels to facilitate erythrocyte maturation. Mechanistically, miR-124 targets TAL1 and c-MYB, two transcription factors involved in normal erythropoiesis. Importantly, this QKI5-mediated regulation also gives rise to a unique miRNA signature, which is required for erythroid differentiation. Taken together, these results demonstrate the pivotal role of QKI5 in primary miRNA processing during erythropoiesis and provide new insights into how a distal element on primary transcripts affects miRNA biogenesis.

Adaptive Control of Semi-Autonomous Teleoperation System With Asymmetric Time-Varying Delays and Input Uncertainties.

This paper addresses the adaptive task-space bilateral teleoperation for heterogeneous master and slave robots to guarantee stability and tracking performance, where a novel semi-autonomous teleoperation framework is developed to ensure the safety and enhance the efficiency of the robot in remote site. The basic idea is to stabilize the tracking error in task space while enhancing the efficiency of complex teleoperation by using redundant slave robot with subtask control. To unify the study of the asymmetric time-varying delays, passive/nonpassive exogenous forces, dynamic parameter uncertainties and dead-zone input in the same framework, a novel switching technique-based adaptive control scheme is investigated, where a special switched error filter is developed. By replacing the derivatives of position errors with their filtered outputs in the coordinate torque design, and employing the multiple Lyapunov-Krasovskii functionals method, the complete closed-loop master (slave) system is proven to be state-independent input-to-output stable. It is shown that both the position tracking errors in task space and the adaptive parameter estimation errors remain bounded for any bounded exogenous forces. Moreover, by using the redundancy of the slave robot, the proposed teleoperation framework can autonomously achieve additional subtasks in the remote environment. Finally, the obtained results are demonstrated by the simulation.

On Input-to-State Stability of Switched Stochastic Nonlinear Systems Under Extended Asynchronous Switching.

An extended asynchronous switching model is investigated for a class of switched stochastic nonlinear retarded systems in the presence of both detection delay and false alarm, where the extended asynchronous switching is described by two independent and exponentially distributed stochastic processes, and further simplified as Markovian. Based on the Razumikhin-type theorem incorporated with average dwell-time approach, the sufficient criteria for global asymptotic stability in probability and stochastic input-to-state stability are given, whose importance and effectiveness are finally verified by numerical examples.

Neural Network-Based Control of Networked Trilateral Teleoperation With Geometrically Unknown Constraints.

Most studies on bilateral teleoperation assume known system kinematics and only consider dynamical uncertainties. However, many practical applications involve tasks with both kinematics and dynamics uncertainties. In this paper, trilateral teleoperation systems with dual-master-single-slave framework are investigated, where a single robotic manipulator constrained by an unknown geometrical environment is controlled by dual masters. The network delay in the teleoperation system is modeled as Markov chain-based stochastic delay, then asymmetric stochastic time-varying delays, kinematics and dynamics uncertainties are all considered in the force-motion control design. First, a unified dynamical model is introduced by incorporating unknown environmental constraints. Then, by exact identification of constraint Jacobian matrix, adaptive neural network approximation method is employed, and the motion/force synchronization with time delays are achieved without persistency of excitation condition. The neural networks and parameter adaptive mechanism are combined to deal with the system uncertainties and unknown kinematics. It is shown that the system is stable with the strict linear matrix inequality-based controllers. Finally, the extensive simulation experiment studies are provided to demonstrate the performance of the proposed approach.