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Manipulating fragile objects in environments such as homes and factories requires stable and gentle grasping along with precise and safe placement. Compared to traditional rigid grippers, the use of soft grippers reduces the control complexity and the risk of damaging objects. However, it is challenging to integrate camera-based optical tactile sensing into a soft gripper without compromising the flexibility and adaptability of the fingers, while also ensuring that the precision of tactile perception remains unaffected by passive deformations of the soft structure during object contact. In this paper, we demonstrate a modular soft two-fingered gripper with a 3D-printed optical tactile sensor (the TacTip) integrated in the palm. We propose a soft-grasping strategy that includes three functions: light contact detection, grasp pose adjustment and loss-of-contact detection, so that objects of different shapes and sizes can be grasped stably and placed precisely, which we test with both artificial and household objects. By sequentially implementing these three functions, the grasp success rate progressively improves from 45% without any functions, to 59% with light contact detection, 90% with grasp pose adjustment, and 97% with loss-of-contact detection, achieving a sub-millimeter placement precision. Overall, this work demonstrates the feasibility and utility of integrating optical tactile sensors into the palm of a soft gripper, of applicability to various types of soft manipulators. The proposed grasping strategy has potential applications in areas such as fragile product processing and home assistance.

Here, we propose an etchless bismuth-substituted yttrium iron garnet layer assisted by a one-dimensional resonant grating waveguide to enhance transverse magneto-optical Kerr effect (TMOKE) via the excitation of quasi-bound state in the continuum. The TMOKE amplitude can be tailored by manipulating the perturbation parameter, and it can reach as high as 1.978, approaching the theoretical maximum value of 2. Additionally, a single-mode temporal coupled-mode theory is employed to further reveal the underlying physical mechanism. It is found that TMOKE is strongly related to the line width of the quasi-BIC resonance and local field enhancement, which are pivotal factors in the design and optimization of photonic devices. As a potential application, we design and numerically demonstrate a refractive index sensor based on the resonantly enhanced TMOKE, with the optimal sensitivity of 110.66 nm/RIU and the corresponding maximum figure of merit of 299.3 RIU$^{-1}$. Our work provides a simple and efficient approach for enhancing TMOKE based on an easy-to-fabricate platform, laying the groundwork for exploring and developing magneto-optical devices such as sensors, magnetic storage devices, and nonreciprocal photonic devices.

T-cell receptors (TCRs) play a crucial role in the immune system by recognizing and binding to specific antigens presented by infected or cancerous cells. Understanding the sequence patterns of TCRs is essential for developing targeted immune therapies and designing effective vaccines. Language models, such as auto-regressive transformers, offer a powerful solution to this problem by learning the probability distributions of TCR repertoires, enabling the generation of new TCR sequences that inherit the underlying patterns of the repertoire. We introduce TCR-GPT, a probabilistic model built on a decoder-only transformer architecture, designed to uncover and replicate sequence patterns in TCR repertoires. TCR-GPT demonstrates an accuracy of 0.953 in inferring sequence probability distributions measured by Pearson correlation coefficient. Furthermore, by leveraging Reinforcement Learning(RL), we adapted the distribution of TCR sequences to generate TCRs capable of recognizing specific peptides, offering significant potential for advancing targeted immune therapies and vaccine development. With the efficacy of RL, fine-tuned pretrained TCR-GPT models demonstrated the ability to produce TCR repertoires likely to bind specific peptides, illustrating RL's efficiency in enhancing the model's adaptability to the probability distributions of biologically relevant TCR sequences.

Recently, vision-based tactile sensors (VBTSs) have gained popularity in robotics systems. The sensing mechanisms of most VBTSs can be categorised based on the type of tactile features they capture. Each category requires specific structural designs to convert physical contact into optical information. The complex architectures of VBTSs pose challenges for traditional manufacturing techniques in terms of design flexibility, cost-effectiveness, and quality stability. Previous research has shown that monolithic manufacturing using multi-material 3D printing technology can partially address these challenges. This study introduces the CrystalTac family, a series of VBTSs designed with a unique sensing mechanism and fabricated through rapid monolithic manufacturing. Case studies on CrystalTac-type sensors demonstrate their effective performance in tasks involving tactile perception, along with impressive cost-effectiveness and design flexibility. The CrystalTac family aims to highlight the potential of monolithic manufacturing in VBTS development and inspire further research in tactile sensing and manipulation., Comment: 32 pages, 12 figures

The metalenses have been extensively studied for their compact and flexible characteristics in focusing and imaging applications. However, it remains a significant challenge to design a broadband achromatic metalens that maintains high efficiency under arbitrary polarization incidence. In this work, we design a broadband achromatic metalens that achieves polarization-independent, high-efficiency focusing by effectively utilizing both co-polarization and cross-polarization components of the transmitted light. Using a minimalist anisotropic nanofin library, we optimize the phase distribution of the metalens at each designed wavelength with the particle swarm algorithm. Numerical simulations demonstrate a stable focal length with a deviation of less than 4$\%$ and an average focusing efficiency of 80.5$\%$ in the visible wavelength range of 450 to 650 nm. Moreover, we design a multi-wavelength off-axis bi-focal metalens to demonstrate the flexible control of output light phase and dispersion achieved by this method. The generality of this design enables its implementation in various metasurface devices, accelerating applications in broadband imaging and virtual/augmented reality.

Time series data in real-world scenarios contain a substantial amount of nonlinear information, which significantly interferes with the training process of models, leading to decreased prediction performance. Therefore, during the time series forecasting process, extracting the local and global time series patterns and understanding the potential nonlinear features among different time observations are highly significant. To address this challenge, we introduce multi-resolution convolution and deformable convolution operations. By enlarging the receptive field using convolution kernels with different dilation factors to capture temporal correlation information at different resolutions, and adaptively adjusting the sampling positions through additional offset vectors, we enhance the network's ability to capture potential nonlinear features among time observations. Building upon this, we propose ACNet, an adaptive convolutional network designed to effectively model the local and global temporal dependencies and the nonlinear features between observations in multivariate time series. Specifically, by extracting and fusing time series features at different resolutions, we capture both local contextual information and global patterns in the time series. The designed nonlinear feature adaptive extraction module captures the nonlinear features among different time observations in the time series. We evaluated the performance of ACNet across twelve real-world datasets. The results indicate that ACNet consistently achieves state-of-the-art performance in both short-term and long-term forecasting tasks with favorable runtime efficiency.

Accurate robotic control over interactions with the environment is fundamentally grounded in understanding tactile contacts. In this paper, we introduce MagicTac, a novel high-resolution grid-based tactile sensor. This sensor employs a 3D multi-layer grid-based design, inspired by the Magic Cube structure. This structure can help increase the spatial resolution of MagicTac to perceive external interaction contacts. Moreover, the sensor is produced using the multi-material additive manufacturing technique, which simplifies the manufacturing process while ensuring repeatability of production. Compared to traditional vision-based tactile sensors, it offers the advantages of i) high spatial resolution, ii) significant affordability, and iii) fabrication-friendly construction that requires minimal assembly skills. We evaluated the proposed MagicTac in the tactile reconstruction task using the deformation field and optical flow. Results indicated that MagicTac could capture fine textures and is sensitive to dynamic contact information. Through the grid-based multi-material additive manufacturing technique, the affordability and productivity of MagicTac can be enhanced with a minimum manufacturing cost of 4.76 GBP and a minimum manufacturing time of 24.6 minutes., Comment: 7 pages, 8 figures, 2024 IEEE International Conference on Robotics and Automation (ICRA 2024)

Tactile sensing is significant for robotics since it can obtain physical contact information during manipulation. To capture multimodal contact information within a compact framework, we designed a novel sensor called ViTacTip, which seamlessly integrates both tactile and visual perception capabilities into a single, integrated sensor unit. ViTacTip features a transparent skin to capture fine features of objects during contact, which can be known as the see-through-skin mechanism. In the meantime, the biomimetic tips embedded in ViTacTip can amplify touch motions during tactile perception. For comparative analysis, we also fabricated a ViTac sensor devoid of biomimetic tips, as well as a TacTip sensor with opaque skin. Furthermore, we develop a Generative Adversarial Network (GAN)-based approach for modality switching between different perception modes, effectively alternating the emphasis between vision and tactile perception modes. We conducted a performance evaluation of the proposed sensor across three distinct tasks: i) grating identification, ii) pose regression, and iii) contact localization and force estimation. In the grating identification task, ViTacTip demonstrated an accuracy of 99.72%, surpassing TacTip, which achieved 94.60%. It also exhibited superior performance in both pose and force estimation tasks with the minimum error of 0.08mm and 0.03N, respectively, in contrast to ViTac's 0.12mm and 0.15N. Results indicate that ViTacTip outperforms single-modality sensors., Comment: 7 pages, 5 figures, 2024 IEEE International Conference on Robotics and Automation (ICRA 2024)

The strong chiral light-matter interaction is crucial for various important fields such as chiral optics, quantum optics, and biomedical optics, driving a quest for the extreme intrinsic chirality assisted by ultrahigh quality ($Q$-) factor resonances. In this quest, we propose a straightforward method to achieve extreme intrinsic chirality in lossless planar structures by manipulating the quasi-BIC through in-plane perturbation. The temporal coupled-mode theory is employed to derive the conditions necessary for achieving maximal intrinsic chirality. The quasi-BIC should be excited within the transparent spectral range of the structure and couple with $x$- and $y$-polarized waves with the same intensity but a phase difference of $\pi$/2. For an illustration, a planar chiral dielectric dimeric waveguide grating is designed that strong interacts with left circularly polarized (LCP) light while decouples from right circularly polarized (RCP) light through in-plane symmetry engineering. Furthermore, by adjusting the magnitude of the in-plane asymmetry, we can independently manipulate the $Q$-factors of the chiral quasi-BIC while maintaining nearly unity circular dichroism. Our results provide a simple yet powerful paradigm for achieving extreme intrinsic chirality on an easily manufacturable platform, which may have potential applications in chiral emission, chiral sensing, and enantiomer separation.

By designing the decentralized time-regularized (Zeno-free) event-triggered strategies for the state-feedback control law, this paper considers the stochastic stabilization of a class of networked control systems, where two sources of randomness exist in multiple decentralized networks that operate asynchronously and independently: the communication channels are constrained by the stochastic network delays and also by Poisson pulsing denial-of-service (Pp-DoS) attacks. The time delay in the network denotes the length from a transmission instant to the corresponding update instant, and is supposed to be a continuous random variable subject to certain continuous probability distribution; while the attacks' cardinal number is a discrete random variable supposed to be subject to Poisson distribution, so the inter-attack time, i.e., the time between two consecutive attack instants, is subject to exponential distribution. The considered system is modeled as a stochastic hybrid formalism, where the randomness enters through the jump map into the reset value (the inter-attack time directly related) of each triggered strategy. By only sampling/transmitting state measurements when needed and simultaneously by taking the specific medium access protocols into account, the designed event-triggered strategies are synthesized in a state-based and decentralized form, which are robust (tolerable well) to stochastic network delays, under different tradeoff-conditions between the minimum inter-event times, maximum allowable delays (i.e., potentially tolerable delays) and the frequencies of attacks. Using stochastic hybrid tools to combine attack-active parts with attack-over parts, the designed triggered strategies, if designed well according to the actual system needs, can tolerate (be resilient to) the Pp-DoS attacks and stochastic network delays without jeopardizing the stability and Zeno-freeness., Comment: 18 pages, 13 figures

Globular clusters harbor numerous millisecond pulsars, but long-period pulsars ($P \gtrsim 100$ ms) are rarely found. In this study, we employed a fast folding algorithm to analyze observational data from multiple globular clusters obtained by the Five-hundred-meter Aperture Spherical radio Telescope (FAST), aiming to detect the existence of long-period pulsars. We estimated the impact of the median filtering algorithm in eliminating red noise on the minimum detectable flux density ($S_{\rm min}$) of pulsars. Subsequently, we successfully discovered two isolated long-period pulsars in M15 with periods approximately equal to 1.928451 seconds and 3.960716 seconds, respectively. On the $P-\dot{P}$ diagram, both pulsars are positioned below the spin-up line, suggesting a possible history of partial recycling in X-ray binary systems disrupted by dynamical encounters later on. According to timing results, these two pulsars exhibit remarkably strong magnetic fields. If the magnetic fields were weakened during the accretion process, then a short duration of accretion might explain the strong magnetic fields of these pulsars., Comment: Accepted by SCIENCE CHINA Physics, Mechanics & Astronomy

This paper introduces the TacFR-Gripper, a reconfigurable Fin Ray-based soft and compliant robotic gripper equipped with tactile skin, which can be used for dexterous in-hand manipulation tasks. This gripper can adaptively grasp objects of diverse shapes and stiffness levels. An array of Force Sensitive Resistor (FSR) sensors is embedded within the robotic finger to serve as the tactile skin, enabling the robot to perceive contact information during manipulation. We provide theoretical analysis for gripper design, including kinematic analysis, workspace analysis, and finite element analysis to identify the relationship between the gripper's load and its deformation. Moreover, we implemented a Graph Neural Network (GNN)-based tactile perception approach to enable reliable grasping without accidental slip or excessive force. Three physical experiments were conducted to quantify the performance of the TacFR-Gripper. These experiments aimed to i) assess the grasp success rate across various everyday objects through different configurations, ii) verify the effectiveness of tactile skin with the GNN algorithm in grasping, iii) evaluate the gripper's in-hand manipulation capabilities for object pose control. The experimental results indicate that the TacFR-Gripper can grasp a wide range of complex-shaped objects with a high success rate and deliver dexterous in-hand manipulation. Additionally, the integration of tactile skin with the GNN algorithm enhances grasp stability by incorporating tactile feedback during manipulations. For more details of this project, please view our website: https://sites.google.com/view/tacfr-gripper/homepage.

The multi-drifting subpulse behaviors in PSR J2007+0910 have been studied carefully with the high sensitivity observations of the Five-hundred-meter Aperture Spherical radio Telescope (FAST) at 1250 MHz. We found that there are at least six different single emission modes in PSR J2007+0910 are observed, four of which show significant subpulse drifting behaviors (modes A, B, C, and D), and the remaining two (modes $E_1$ and $E_2$) show stationary subpulse structures. The subpulse drifting periods of modes A, B, C, and D are $P_{3, A} = 8.7 \pm 1.6 P$, $P_{3, B} = 15.8 \pm 1.2 P$, $P_{3, C} = 21.6 \pm 1.3 P$ and $P_{3, D} = 32.3 \pm 0.9 P$, respectively, where $P$ represents the pulse period of this pulsar. The subpulse separation is almost the same for all modes $P_2 = 6.01 \pm 0.18 ^\circ$. Deep analysis suggests that the appearance and significant changes in the drifting period of multi-drifting subpulse emission modes for a pulsar may originate from the aliasing effect. The observed non-drifting modes may be caused by the spark point move with a period ~P_2. Our statistical analysis shows that the drift mode of this pulsar almost always switches from slower to faster drifts in the mode change. The interesting subpulse emission phenomenon of PSR J2007+0910 provides a unique opportunity to understand the switching mechanism of multi-drift mode., Comment: 10 pages, 10 figures, 2 tables, Accepted to MNRAS

Recent advances in nonlocal metasurfaces have enabled unprecedented success in shaping the wavefront of light with spectral selectivity, offering new solutions for many emerging nanophotonics applications. The ability to tune both the spectral and spatial properties of such a novel class of metasurfaces is highly desirable, but the dynamic nonvolatile control remains elusive. Here, we demonstrate active narrowband wavefront manipulation by harnessing quasi-bound states in the continuum (quasi-BICs) in phase-change nonlocal metasurfaces. The proof-of-principle metasurfaces made of Sb$_2$S$_3$ allow for nonvolatile, reversible, and tunable spectral control over wavefront and switchable spatial response at a given wavelength. The design principle mainly builds upon the combination of the geometry phase of quasi-BICs and the dynamic tunability of phase-change meta-atoms to tailor the spatial response of light at distinct resonant wavelengths. By tuning the crystallization level of Sb$_2$S$_3$ meta-atoms, the dynamic nonlocal wavefront-shaping functionalities of beam steering, 1D, and 2D focusing are achieved. Furthermore, we demonstrate tunable holographic imaging with active spectral selectivity using our phase-change nonlocal metasurface. This work represents a critical advance towards developing integrated dynamic nonlocal metasurface for future augmented and virtual reality wearables.

We report detections of scintillation arcs for pulsars in globular clusters M5, M13 and M15 for the first time using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). From observations of these arcs at multiple epochs, we infer that screen-like scattering medium exists at distances $4.1_{-0.3}^{+0.2}$ kpc, $6.7_{-0.2}^{+0.2}$ kpc and $1.3_{-1.0}^{+0.7}$ kpc from Earth in the directions of M5, M13 and M15, respectively. This means M5's and M13's scattering screens are located at $3.0_{-0.2}^{+0.1}$ kpc and $4.4_{-0.1}^{+0.1}$ kpc above the galactic plane, whereas, M15's is at $0.6_{-0.5}^{+0.3}$ kpc below the plane. We estimate the scintillation timescale and decorrelation bandwidth for each pulsar at each epoch using the one-dimensional auto-correlation in frequency and time of the dynamic spectra. We found that the boundary of the Local Bubble may have caused the scattering of M15, and detected the most distant off-plane scattering screens to date through pulsar scintillation, which provides evidence for understanding the medium circulation in the Milky Way., Comment: 9 pages, 3 figures, matches the published version

Segmenting and recognizing surgical operation trajectories into distinct, meaningful gestures is a critical preliminary step in surgical workflow analysis for robot-assisted surgery. This step is necessary for facilitating learning from demonstrations for autonomous robotic surgery, evaluating surgical skills, and so on. In this work, we develop a hierarchical semi-supervised learning framework for surgical gesture segmentation using multi-modality data (i.e. kinematics and vision data). More specifically, surgical tasks are initially segmented based on distance characteristics-based profiles and variance characteristics-based profiles constructed using kinematics data. Subsequently, a Transformer-based network with a pre-trained `ResNet-18' backbone is used to extract visual features from the surgical operation videos. By combining the potential segmentation points obtained from both modalities, we can determine the final segmentation points. Furthermore, gesture recognition can be implemented based on supervised learning. The proposed approach has been evaluated using data from the publicly available JIGSAWS database, including Suturing, Needle Passing, and Knot Tying tasks. The results reveal an average F1 score of 0.623 for segmentation and an accuracy of 0.856 for recognition., Comment: 8 pages, 7 figures. Accepted by 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023). For more details about this paper, please visit our website: \url{https://sites.google.com/view/surseg/home}

High-resolution tactile sensing can provide accurate information about local contact in contact-rich robotic tasks. However, the deployment of such tasks in unstructured environments remains under-investigated. To improve the robustness of tactile robot control in unstructured environments, we propose and study a new concept: \textit{tactile saliency} for robot touch, inspired by the human touch attention mechanism from neuroscience and the visual saliency prediction problem from computer vision. In analogy to visual saliency, this concept involves identifying key information in tactile images captured by a tactile sensor. While visual saliency datasets are commonly annotated by humans, manually labelling tactile images is challenging due to their counterintuitive patterns. To address this challenge, we propose a novel approach comprised of three interrelated networks: 1) a Contact Depth Network (ConDepNet), which generates a contact depth map to localize deformation in a real tactile image that contains target and noise features; 2) a Tactile Saliency Network (TacSalNet), which predicts a tactile saliency map to describe the target areas for an input contact depth map; 3) and a Tactile Noise Generator (TacNGen), which generates noise features to train the TacSalNet. Experimental results in contact pose estimation and edge-following in the presence of distractors showcase the accurate prediction of target features from real tactile images. Overall, our tactile saliency prediction approach gives robust sim-to-real tactile control in environments with unknown distractors. Project page: https://sites.google.com/view/tactile-saliency/., Comment: Accepted by IROS 2023

Object pushing presents a key non-prehensile manipulation problem that is illustrative of more complex robotic manipulation tasks. While deep reinforcement learning (RL) methods have demonstrated impressive learning capabilities using visual input, a lack of tactile sensing limits their capability for fine and reliable control during manipulation. Here we propose a deep RL approach to object pushing using tactile sensing without visual input, namely tactile pushing. We present a goal-conditioned formulation that allows both model-free and model-based RL to obtain accurate policies for pushing an object to a goal. To achieve real-world performance, we adopt a sim-to-real approach. Our results demonstrate that it is possible to train on a single object and a limited sample of goals to produce precise and reliable policies that can generalize to a variety of unseen objects and pushing scenarios without domain randomization. We experiment with the trained agents in harsh pushing conditions, and show that with significantly more training samples, a model-free policy can outperform a model-based planner, generating shorter and more reliable pushing trajectories despite large disturbances. The simplicity of our training environment and effective real-world performance highlights the value of rich tactile information for fine manipulation. Code and videos are available at https://sites.google.com/view/tactile-rl-pushing/., Comment: Accepted by IEEE Robotics and Automation Letters (RA-L)

Bimanual manipulation with tactile feedback will be key to human-level robot dexterity. However, this topic is less explored than single-arm settings, partly due to the availability of suitable hardware along with the complexity of designing effective controllers for tasks with relatively large state-action spaces. Here we introduce a dual-arm tactile robotic system (Bi-Touch) based on the Tactile Gym 2.0 setup that integrates two affordable industrial-level robot arms with low-cost high-resolution tactile sensors (TacTips). We present a suite of bimanual manipulation tasks tailored towards tactile feedback: bi-pushing, bi-reorienting and bi-gathering. To learn effective policies, we introduce appropriate reward functions for these tasks and propose a novel goal-update mechanism with deep reinforcement learning. We also apply these policies to real-world settings with a tactile sim-to-real approach. Our analysis highlights and addresses some challenges met during the sim-to-real application, e.g. the learned policy tended to squeeze an object in the bi-reorienting task due to the sim-to-real gap. Finally, we demonstrate the generalizability and robustness of this system by experimenting with different unseen objects with applied perturbations in the real world. Code and videos are available at https://sites.google.com/view/bi-touch/., Comment: Accepted by IEEE Robotics and Automation Letters (RA-L)

Multi-robot platforms are playing an increasingly important role in warehouse automation for efficient goods transport. This paper proposes a novel customization of a multi-robot system, called Tactile Mobile Manipulators (TacMMs). Each TacMM integrates a soft optical tactile sensor and a mobile robot with a load-lifting mechanism, enabling cooperative transportation in tasks requiring coordinated physical interaction. More specifically, we mount the TacTip (biomimetic optical tactile sensor) on the Distributed Organisation and Transport System (DOTS) mobile robot. The tactile information then helps the mobile robots adjust the relative robot-object pose, thereby increasing the efficiency of load-lifting tasks. This study compares the performance of using two TacMMs with tactile perception with traditional vision-based pose adjustment for load-lifting. The results show that the average success rate of the TacMMs (66%) is improved over a purely visual-based method (34%), with a larger improvement when the mass of the load was non-uniformly distributed. Although this initial study considers two TacMMs, we expect the benefits of tactile perception to extend to multiple mobile robots. Website: https://sites.google.com/view/tacmms, Comment: 8 pages, accepted in IEEE Robotics and Automation Letters, 19 June 2023

Developing practical quantum technologies will require the exquisite manipulation of fragile systems in a robust and repeatable way. As quantum technologies move towards real world applications, from biological sensing to communication in space, increasing experimental complexity introduces constraints that can be alleviated by the introduction of new technologies. Robotics has shown tremendous progress in realising increasingly smart, autonomous and highly dexterous machines. Here, we demonstrate that a robotic arm equipped with a magnet can sensitise an NV centre quantum magnetometer in challenging conditions unachievable with standard techniques. We generate vector magnetic field with $1^\circ$ angular and 0.1 mT amplitude accuracy and determine the orientation of a single stochastically-aligned spin-based sensor in a constrained physical environment. Our work opens up the prospect of integrating robotics across many quantum degrees of freedom in constrained settings, allowing for increased prototyping speed, control, and robustness in quantum technology applications., Comment: 11 pages, 4 figures. Small correction in first paragraph

The magneto-optical Faraday and Kerr effects are widely used in modern optical devices. In this letter, we propose an all-dielectric metasurface composed of perforated magneto-optical thin films, which can support the highly confined toroidal dipole resonance and provide full overlap between the localized electromagnetic field and the thin film, and consequently enhance the magneto-optical effects to an unprecedented degree. The numerical results based on finite element method show that the Faraday and Kerr rotations can reach -13.59${\deg}$ and 8.19${\deg}$ in the vicinity of toroidal dipole resonance, which are 21.2 and 32.8 times stronger than those in the equivalent thickness of thin films, respectively. In addition, we design an environment refractive index sensor based on the resonantly enhanced Faraday and Kerr rotations, with sensitivities of 62.96 nm/RIU and 73.16 nm/RIU, and the corresponding maximum figures of merit 132.22${\deg}$/RIU and 429.45${\deg}$/RIU, respectively. This work provides a new strategy for enhancing the magneto-optical effects at nanoscale, and paves the way for the research and development of magneto-optical metadevices such as sensors, memories, and circuits.

Microsurgery involves the dexterous manipulation of delicate tissue or fragile structures such as small blood vessels, nerves, etc., under a microscope. To address the limitation of imprecise manipulation of human hands, robotic systems have been developed to assist surgeons in performing complex microsurgical tasks with greater precision and safety. However, the steep learning curve for robot-assisted microsurgery (RAMS) and the shortage of well-trained surgeons pose significant challenges to the widespread adoption of RAMS. Therefore, the development of a versatile training system for RAMS is necessary, which can bring tangible benefits to both surgeons and patients. In this paper, we present a Tactile Internet-Based Micromanipulation System (TIMS) based on a ROS-Django web-based architecture for microsurgical training. This system can provide tactile feedback to operators via a wearable tactile display (WTD), while real-time data is transmitted through the internet via a ROS-Django framework. In addition, TIMS integrates haptic guidance to `guide' the trainees to follow a desired trajectory provided by expert surgeons. Learning from demonstration based on Gaussian Process Regression (GPR) was used to generate the desired trajectory. User studies were also conducted to verify the effectiveness of our proposed TIMS, comparing users' performance with and without tactile feedback and/or haptic guidance., Comment: 8 pages, 7 figures. For more details of this project, please view our website: https://sites.google.com/view/viewtims/home

The accelerating aging population has led to an increasing demand for domestic robotics to ease caregivers' burden. The integration of Internet of Things (IoT), robotics, and human-robot interaction (HRI) technologies is essential for home-care applications. Although the concept of the Internet of Robotic Things (IoRT) has been utilized in various fields, most existing IoRT frameworks lack ergonomic HRI interfaces and are limited to specific tasks. This paper presents an open-source unified Internet of Humans and Robotic Things (IoHRT) framework with cloud computing, which combines personalized HRI interfaces with intelligent robotics and IoT techniques. This proposed open-source framework demonstrates characteristics of high security, compatibility, and modularity, allowing unlimited user access. Two case studies were conducted to evaluate the proposed framework's functionalities, evaluating its effectiveness in home-care scenarios. Users' feedback was collected via questionnaires, which indicates the IoHRT framework's high potential for home-care applications.

Tactile pose estimation and tactile servoing are fundamental capabilities of robot touch. Reliable and precise pose estimation can be provided by applying deep learning models to high-resolution optical tactile sensors. Given the recent successes of Graph Neural Network (GNN) and the effectiveness of Voronoi features, we developed a Tactile Voronoi Graph Neural Network (Tac-VGNN) to achieve reliable pose-based tactile servoing relying on a biomimetic optical tactile sensor (TacTip). The GNN is well suited to modeling the distribution relationship between shear motions of the tactile markers, while the Voronoi diagram supplements this with area-based tactile features related to contact depth. The experiment results showed that the Tac-VGNN model can help enhance data interpretability during graph generation and model training efficiency significantly than CNN-based methods. It also improved pose estimation accuracy along vertical depth by 28.57% over vanilla GNN without Voronoi features and achieved better performance on the real surface following tasks with smoother robot control trajectories. For more project details, please view our website: https://sites.google.com/view/tac-vgnn/home, Comment: 7 pages, 10 figures, accepted by 2023 IEEE International Conference on Robotics and Automation (ICRA)

This paper reviews the attitude control problems for rigid-body systems, starting from the attitude representation for rigid body kinematics. Highly redundant rotation matrix defines the attitude orientation globally and uniquely by 9 parameters, which is the most fundamental one, without any singularities; minimum 3-parameter Euler angles or (modified) Rodrigues parameters define the attitude orientation neither globally nor uniquely, but the former exhibits kinematical singularity and Gimbal lock, while the latter two exhibit geometrical singularity; once-redundant axis-angle or unit quaternion globally define the attitude rotation but not uniquely using 4 parameters, but the former is not appropriate to define very small or very large rotations, while the latter shows unwinding phenomenon despite of the reduced computation burden. In addition, we explore the relationships among those attitude representations, including the connections among Gimbal lock, unwinding phenomenon and a nowhere dense set of zero Lebesgue measure. Based on attitude representations, we analyze different attitude control laws, almost global control and global attitude control, nominal and general robustness, as well as the technique tools., Comment: 13 pages, 6 figures, 2 tables

Stochastic hybrid inclusions (SHIs) address situations with the stochastic continuous evolution in a stochastic differential inclusions and random jumps in the difference inclusions due to the forced (the state reaching a boundary in the state space) and/or spontaneous (the state vector may occur spontaneously) transitions. An obvious characteristic of SHIs is the non-uniqueness of random solutions, which can be ensured by the mild regularity conditions, as well as nominal robustness. Basic sufficient conditions for stability/recurrence in probability are usually expressed based on different types of Lyapunov functions, including Lagrange/Lyapunov/Lyapunov-Forster functions respectively for Lagrange/Lyapunov/asymptotical stability in probability and Foster/Lagrange-Forster functions for recurrence, (weaker) relaxed Lyapunov-based sufficient conditions including Matrosov-Foster functions and the stochastic invariance principle, as well as Lyapunov-based necessary and sufficient conditions for asymptotical stability in probability or recurrence (i.e.,converse theorems), etc. The converse theorems involving smooth Lyapunov functions are guaranteed by the sequential compactness and thus robustness. In addition, the uniformity property and causality are analyzed for the stabilities in probability. Hence, serving as a partial roadmap for the theoretical development of SHIs, also serving as inspiration, we anticipate that many of the open questions, including the prediction problem, the filtering problem and the control problem, will be resolved based on the techniques of SHIs., Comment: 15 pages, 3 figures, 1 table

Traditional deep learning-based visual imitation learning techniques require a large amount of demonstration data for model training, and the pre-trained models are difficult to adapt to new scenarios. To address these limitations, we propose a unified framework using a novel progressive learning approach comprised of three phases: i) a coarse learning phase for concept representation, ii) a fine learning phase for action generation, and iii) an imaginary learning phase for domain adaptation. Overall, this approach leads to a one-shot domain-adaptive imitation learning framework. We use robotic pouring task as an example to evaluate its effectiveness. Our results show that the method has several advantages over contemporary end-to-end imitation learning approaches, including an improved success rate for task execution and more efficient training for deep imitation learning. In addition, the generalizability to new domains is improved, as demonstrated here with novel background, target container and granule combinations. We believe that the proposed method can be broadly applicable to different industrial or domestic applications that involve deep imitation learning for robotic manipulation, where the target scenarios have high diversity while the human demonstration data is limited., Comment: under review

Human-robot shared control, which integrates the advantages of both humans and robots, is an effective approach to facilitate efficient surgical operation. Learning from demonstration (LfD) techniques can be used to automate some of the surgical subtasks for the construction of the shared control framework. However, a sufficient amount of data is required for the robot to learn the manoeuvres. Using a surgical simulator to collect data is a less resource-demanding approach. With sim-to-real adaptation, the manoeuvres learned from a simulator can be transferred to a physical robot. To this end, we propose a sim-to-real adaptation method to construct a human-robot shared control framework for robotic surgery. In this paper, a desired trajectory is generated from a simulator using LfD method, while dynamic motion primitives (DMPs) based method is used to transfer the desired trajectory from the simulator to the physical robotic platform. Moreover, a role adaptation mechanism is developed such that the robot can adjust its role according to the surgical operation contexts predicted by a neural network model. The effectiveness of the proposed framework is validated on the da Vinci Research Kit (dVRK). Results of the user studies indicated that with the adaptive human-robot shared control framework, the path length of the remote controller, the total clutching number and the task completion time can be reduced significantly. The proposed method outperformed the traditional manual control via teleoperation., Comment: Accepted by 2022ICRA

In recent decades, the tremendous benefits surgical robots have brought to surgeons and patients have been witnessed. With the dexterous operation and the great precision, surgical robots can offer patients less recovery time and less hospital stay. However, the controls for current surgical robots in practical usage are fully carried out by surgeons via teleoperation. During the surgery process, there exists a lot of repetitive but simple manipulation, which can cause unnecessary fatigue to the surgeons. In this paper, we proposed a deep reinforcement learning-based semi-autonomous control framework for robotic surgery. The user study showed that the framework can reduce the completion time by 19.1% and the travel length by 58.7%.

Finite $p$-groups with a unique $\mathcal{A}_2$-subgroup are classified up to isomorphism. A problem proposed by Berkovich and Janko is solved.