Miracle-dz/UniFAR-FTUs huggingface dataset:
A Sound Source Tracking Humanoid Robotic Platform: Design and Architecture

Sound source localization is an active field of research with multiple paradigms of sound acquisition and processing. The design of a motorized platform for sound source tracking is presented and discussed in this paper, showing its components and operation. It consists of a humanoid head equipped with microphones and actuated by servomotors to have the orientation towards a source emitting sound in its environment. The design is inspired from the human binaural audition but extends to 4 microphones in order to allow an efficient estimation of the sound direction of arrival in horizontal and vertical planes.
Orbital Stabilization of Underactuated Systems Using Time Period Regulation

A new approach to orbital stabilization of under-actuated systems with one passive degree-of-freedom (DOF) is presented. Virtual holonomic constraints are enforced using partial feedback linearization; this results in a dense set of periodic orbits on a constraint manifold. Every orbit on the constraint manifold is associated with a unique time-period. A desired orbit is selected and the impulse controlled Poincaré map (ICPM) approach is utilized to stabilize the orbit by regulating the time-period. By treating the time period as the output, it is possible to design a dead-beat controller that achieves orbital stabilization in a single time-step. The effectiveness of the dead-beat design is demonstrated for the cart-pendulum system.
HEV Energy Management Strategy Based on TD3 with Prioritized Exploration and Experience Replay

This paper presents a novel energy management strategy for hybrid electric vehicles (HEVs) that is based on an expert twin-delayed deep deterministic policy gradient with prioritized exploration and experience replay (TD3-PEER). State-of-the-art TD3 requires critic networks to generate predicted Q value for state-action pairs to update a policy network. However, the critic networks may struggle with predicting Q values for certain states when the Q values of these states are sensitive to action selection. To address this issue, this paper proposes a prioritized exploration technique that encourages the agent to visit action-sensitive states more frequently in the application of HEV energy management. The proposed algorithm is tested and validated on a P0+P4 HEV model. To simplify the control design, a motor activation threshold is introduced into the final layer of the agent’s actor. In addition, dynamic programming results are incorporated into the training of the TD3, helping the agent avoid inefficient operations. Simulation results demonstrate that with expert knowledge considered for all learning-based methods, the proposed TD3-PEER outperforms other RL-based energy management strategies, including DDPG-PER and deep Q-network, by an average of 2.3% and 3.74% over the training and validation cycles, respectively.
KubeROS: A Unified Platform for Automated and Scalable Deployment of ROS2-based Multi-Robot Applications

As advanced algorithms enable robots to handle more challenging tasks and operate more autonomously, the on-board computer cannot meet the increased demands regarding computing power and memory storage in an efficient way. Leveraging the massive computing power of the cloud and low-latency connectivity to the edge can compensate for this lack of computing resources. However, this introduces a new challenge related to the deployment of complex robotic software across multiple devices, especially in a large-scale system. This paper presents KubeROS, a unified and fully managed platform for automated deployment of robotic applications developed on top of Robot Operating System 2 (ROS2), in a hybrid computing infrastructure with robots, edge and cloud. KubeROS uses Kubernetes from Cloud Native Computing as its underlying software orchestration framework. It aims to help researchers and developers with no prior cloud computing knowledge deploy their ROS2-based robotic applications at any scale. KubeROS eliminates the need for system configuration and network setup. We demonstrate the applicability of KubeROS by deploying a fleet of simulated mobile manipulators in a clas-sical pick-and-place application. The experiments demonstrate the effects of different deployment strategies for vision-based motion planning under different fleet sizes and workloads. In addition, KubeROS improves task performance by using high-performance computing at the edge and in the cloud, and achieves high resource efficiency when using the shared deployment strategy.
Fruit Tracking Over Time Using High-Precision Point Clouds

Monitoring the traits of plants and fruits is a fundamental task in horticulture. With accurate measurements, farmers can predict the yield of their crops and use this information for making informed management decisions, and breeders can use it for variety selection. Agricultural robotic applications promise to automate this monitoring task. In this paper, we address the problem of monitoring fruit growth and investigate the matching of fruits recorded in commercial greenhouses at different growth stages based on data recorded from terrestrial laser scanners. This is challenging as fruits appear highly similar, change over time, and are subject to severe occlusions. We first propose a fruit descriptor, which captures the topology of the fruit surroundings to facilitate the matching between different points in time. We capture and describe the relationship between a fruit and its neighbors such that our descriptors are less affected by the growth over time. Furthermore, we define a matching cost function and use an optimal assignment algorithm to match the fruit observations taken in different weeks. The experiments show that our descriptor achieves a high spatio-temporal matching accuracy, which is superior to the commonly used geometric point cloud descriptors.
ROSMC: A High-Level Mission Operation Framework for Heterogeneous Robotic Teams

Heterogeneous teams of multiple mobile robots will be important for future scientific explorations of extraterrestrial surfaces or hazardous areas. Mission operation in such harsh, unknown environments poses diverse challenges. Robots need to cooperate autonomously due to the large network latency to the ground station while operators need to adapt the ongoing mission flexibly based on new discoveries obtained during execution. Furthermore, shared situational awareness between operators and roboticists is highly required to deal with execution failures promptly. To overcome these challenges, this paper proposes the high-level mission operation framework ROSMC. The concept of mission synchronization to robots enables continuous mission adaptations and future planning by operators while robots execute the mission autonomously. The ROS-based GUIs enable operators to intuitively create and monitor the mission for robots as well as to communicate with roboticists smoothly. The proposed framework was evaluated by a pilot study with a simulator and demonstrated at a Moon-analogue field on Mt. Etna in Sicily, Italy, involving 3 robots and around 70 researchers for 4 weeks.
Reinforcement Learning with Probabilistically Safe Control Barrier Functions for Ramp Merging

Prior work has looked at applying reinforcement learning (RL) approaches to autonomous driving scenarios, but the safety of the algorithm is often compromised due to instability or the presence of ill-defined reward functions. With the use of control barrier functions embedded into the RL policy, we arrive at safe policies to optimize the performance of the autonomous driving vehicle through the advantage of a safety layer over the RL methods to ease the design of reward functions. However, control barrier functions need a good approximation of the model of the system. We use probabilistic control barrier functions [4] to account for model uncertainty. Our Safety-Assured Policy Optimization - Ramp Merging (SAPO-RM) algorithm is implemented online in the CARLA [1] Simulator and offline on the US I-80 dataset extracted from the NGSIM Database provided by NHTSA [2]. We further test the algorithm and perform ablation studies of it on the US-101 and exi-D datasets to compare the approaches. The proposed algorithm can also be applied to other driving scenarios by changing the reward and safety constraints.
Electromagnetic Clutch-Based Ankle Exosuit for Assisting Stroke Survivors With Different Body Sizes

Exosuits can be effective in aiding stroke rehabilitation. However, single-motor exosuits are challenging to adapt to users with different body sizes. Methods: This letter proposes an electromagnetic clutch-based ankle exosuit (ECAE) for stroke survivors with different body sizes. This exosuit includes a single-motor actuation unit, two novel electromagnetic ratchet clutches, and functional textiles. Compared with other ankle rehabilitation robots, this exsouit could adapt to different users in terms of adaptive preload after wearing, maintaining device transparency when unpowered, and providing timely assistance. Experiments: Benchtop experiments were constructed to evaluate the property of designed clutches. Fifteen healthy subjects with different body sizes wore the ECAE to verify its adaptive performance. Then, they walked with unpowered and powered exosuits to validate transparency and timely assistive performance, respectively. Results: Designed clutches have a fast switching response (<20 ms). The ECAE could adaptively adjust the cable length with a slight preload (0.32–1.31 N) for different users. Compared with this exosuit without designed clutches, device transparency is increased by 84.58% (P < 0.001), and the assistive delay time is reduced by 57.77% (P < 0.004). Conclusion: This ECAE could adapt to different users without needing customization, which could reduce the price of exosuits.
Control Lyapunov-Barrier function-based predictive control using a deep hybrid model with guarantees on domain of applicability

The domain of applicability (DA) of a data-driven model is limited by its training data. Consequently, the DA of a hybrid model which combines a first-principles model with a data-driven model is also limited by its training data even though it has better extrapolation capabilities compared to a data-based model. Nonetheless, the domain of applicability (DA) of a hybrid model is finite and should be taken into account when developing a hybrid model-based predictive controller in order to maximize its performance. To this end, a Control Lyapunov-Barrier Function-based model predictive controller (CLBF-based MPC) is developed which utilizes a deep hybrid model (DHM), i.e., a deep neural network (DNN) combined with a first-principles model. Additionally, theoretical guarantees are provided on stability as well as on system states to stay within the DA of the DHM. The efficacy of the proposed framework is demonstrated on a continuous stirred tank reactor.
Twist Snake: Plastic table-top cable-driven robotic arm with all motors located at the base link

Table-top robotic arms for education and research must be low-cost for availability and lightweight and soft for safety. Therefore, as such a robot, this study focuses on designing a plastic table-top cable-driven robotic arm with all motors located at the base link. However, locating all motors at the base link results in a significant distance between a driving motor and driven joint, increases the number of parts for the force transmission, and increases the risk of a cable loosening and coming off of a pulley. To overcome these issues, this study proposed a novel cable-driven robotic arm named Twist Snake. We designed a joint composition of Twist Snake to minimize the number of parts for the force transmission. In addition, it has a compact cable-pretension/termination-mechanism and covering parts to prevent the cable from loosening and coming off of the pulley. The arm comprised 475 mm long moving links with an 802 g. The feasibility of the arm was experimentally demonstrated by contact rich tasks, the insertion of a toy peg into a hole and swiping a whiteboard with a cleaner. The optimization of the proposed design and the development of a learning method for the arm that leverages contact will be investigated in future work.
A Moving Target Tracking System of Quadrotors with Visual-Inertial Localization

This paper implements a vision-based moving target tracking system of quadrotors with visual-inertial localization in GNSS-denied indoor environments. We use the visual-inertial odometry to estimate the states of the UAV by minimizing visual and inertial residuals, and estimate the states of the target with extended Kalman Filter from visual detection. This research formulates the target tracking problem as optimization-based trajectory generation where a weighted sum cost function jointly penalizes the tracking error, the control cost of the trajectory and the trajectory length, while enforcing the safety and feasibility constraints. We present a strategy that represents the trajectory as piecewise Bézier curves using Bernstein polynomial basis. Due to the special properties of Bézier curves, the position of the entire trajectory and its derivatives can be directly bounded within the safe spaces, thus this facilitating the dynamics of the quadrotor. The proposed strategy can generate smooth and collision-free tracking trajectories and is time and space efficient. We conduct simulations and real-world experiments to validate the effectiveness of our system.
UBMDP: Urban Building Mesh Decoupling and Polygonization

With the development of photogrammetry, digital city, and metaverse, the 3-D representation of urban buildings has attracted more and more attention. As the main form of the 3-D urban building model, the triangular mesh model has deficiencies such as high complexity, high-data volume, and low-structural information, which seriously restrict its application in spatial analysis and urban planning. This article proposes a hybrid modeling strategy geared toward the mesh model generated from oblique images to obtain building models that are compact, manifold, watertight, and have certain structural and semantic information. First of all, when the planar region topology graph has been established, a topology decoupling strategy is designed to obtain a set of relatively independent topology subgraphs which form a hierarchical structure. After that, to improve model quality, topology optimization of parallel planes has also been studied systematically. Then, we adopt a divide-and-conquer strategy to perform data-driven and model-driven building modeling for the primary and ancillary structures. Finally, a component-level simple polygon model combination is generated. Experiments prove that the proposed method has excellent visual authenticity, structural completeness advantages, and decent LoD3 ability. As a mesh simplification method, the data is compressed to 0.11%–0.75% in a Hausdorff metric around 0.3 m, which further proves that this method is state-of-the-art.
GSNet: Model Reconstruction Network for Category-level 6D Object Pose and Size Estimation

Category-level 6D pose and size estimation is to estimate the rotation, translation and size of the observed instance objects from an arbitrary angle in a cluttered scene. Compared with instance-level 6D pose estimation, there are two main challenges for category-level 6D pose estimation. One is that the algorithm needs to estimate the 6D pose and size of unseen objects, and no 3D models are available. Another is that different instance objects of the same class of objects differ greatly in shape. This paper propose a novel method to estimate the 6D pose and size of unseen objects from an RGB-D image. To handle intra-class shape variation, we propose an autoencoder-decoder that is trained on a set of object models to learn structural feature-invariant and shape-variant features of intra-class objects, and constructs a category-level priori model containing the structure feature and shape feature. To solve the problem of 3D model, this paper proposes a model reconstruction network including 3D graph convolution and spherical convolution (GSNet), which can reconstruct the 3D model of the observed instance object from the input RGB-D image and the priori model, and establish a dense correspon-dence between the 3D model and the observed instance object. Finally, random sample consensus (RANSAC) algorithm and Umeyama algorithm are used to estimate the 6D pose and size of the object. Extensive experiments on benchmark datasets show that the proposed method achieves state-of-the-art performance in category-level 6D object pose estimation. In order to prove that our method can be applied to the grasping and operation tasks of robots in industry and life, we deploy our method to a physical UR5 robot to perform grasping tasks on unseen but category known instances, and the results validate the efficacy of our proposed method.
A Deep Learning Framework for Denoising MRI Images using Autoencoders

Magnetic Resonance Imaging (MRI) has become an indispensable tool in the medical field for diagnosing and monitoring various diseases and conditions. However, the quality of MRI images can be degraded by noise, which can lead to inaccurate interpretations and diagnoses. In recent years, machine learning techniques have shown great potential in enhancing the accuracy of image denoising, especially in the medical domain. In this study, we propose a novel deep learning model based on autoencoders for denoising MRI images. We employed various preprocessing techniques such as data augmentation, resizing, normalization, and conventional denoising methods on our MRI images. Our model comprises a convolutional neural network autoencoder (CCNAE), which we fine-tuned by testing different parameters and layers to achieve optimal performance. Our results demonstrate a validation loss of approximately 0.0001, indicating a substantial improvement in denoising performance. Our work represents an important step towards developing an efficient and effective method for denoising MRI images without compromising critical data or time.
A New Zero Knowledge Argument for General Circuits and Its Application

Verifying the correctness of computation without revealing the input is a critical issue intensively studied in real-world applications. The recent surge of zero knowledge arguments has been focusing on its efficiency and practicality. Among them, GKR-based arguments have received wide attention and become the foundation of many zero-knowledge proof protocols. However, GKR-based protocols are restricted to layered arithmetic circuits. We propose Terrace, a new, efficient zero-knowledge argument system for general circuits, based on GKR. By dynamically patching cross-layer claims to the original circuit for verification instead of verifying those claims separately, Terrace is able to reduce the total circuit size and thus enjoys a logarithmic factor less verification time and proof size. Terrace is further extended to include the verification of non-arithmetic operations by rewriting those claims in the multilinear extension form. Experimental results demonstrate that Terrace enjoys a competitive performance on efficiency, and shows great promise in enabling low-cost verification of neural networks.
Z' and W' gauge bosons in SU(2)xSU(2)xU(1) models : Collider phenomenology at LO and NLO QCD

General SU(2)x SU(2)x U(1) models represent a well-motivated intermediate step towards the unication of the Standard Model (SM) gauge groups. Extended gauge group sector, as compared to that of the SM, leads to additional neutral and charged gauge bosons. These so-called Z' and W' bosons are actively searched for at the Large Hadron Collider (LHC). Based on a recent global analysis of low-energy and LEP constraints of these models, we perform numerical scans of their various signals at the LHC at Leading Order accuracy. We show that total cross sections for lepton and third-generation quark pairs, while experimentally easily accessible, provide individually only partial information about the model realized in Nature. In contrast, correlations of these cross sections in the neutral and charged current channels may well lead to a unique identification. Subsequently we study the electroweak top-pair production at Next-to-leading Order (NLO) accuracy in the SM extensions with an additional Z' boson assuming general flavour-diagonal couplings. We calculate the virtual and real corrections at order O(alS*alW^2) and implement them in the POWHEG BOX framework which allows for consistent matching of NLO QCD calculations with parton showers. We find that the NLO corrections can be very important but the K-factors in the invariant mass region around the resonance mass are modest.
Motion Capture System based on RGB Camera for Human Walking Recognition using Marker-based and Markerless for Kinematics of Gait

The motion capture system has the potential to perform kinematics of gait analysis. Gait analysis can be applied in human activity recognition (HAR) for human walking recognition technology. The walking recognition makes it challenging for researchers to develop using RGB camera with high accuracy. This paper compares the accuracy of vision-based motion capture based on an RGB Camera using marker-based and markerless methods. The evaluation to determine the accuracy of the proposed of both methods was compared with statistical analysis. The marker-based method uses the Kalman filter, and the markerless method uses MediaPipe to measure gait parameters. Development of motion capture that can detect joint leg positions and measure joint angles based on OpenCV. It is designed for joint trajectories and angles at the hip, knee, and ankle. The motion capture system is implemented by a Logitech C270 webcam, Intel core i5 2.1 GHz processor, 8 GB RAM, and processed by JupyterLab with Python programming. It has been tested on recorded video data containing the subject walking straight with three gait cycles: slow, fast, and zigzag. In the marker-based method, each movement’s average joint position detection errors are 22 pixels, 134 pixels, and 50 pixels. The angles of the hip and knee joints have an average angle difference with a reference of ±7°. In comparison, the markerless method has an average position error are 23 pixels, 65 pixels, and 49 pixels. And markerless has an average angle difference with a reference of ±5°.
Assessing the Accuracy and Reliability of a Low-Cost Virtual Reality Headset versus a Goniometer for Measuring Cervical Range of Motion

Axial spondyloarthritis (axSpA) is one of the prevalent reasons for neck pain, stiffness and mobility issues. Neck pain is assessed by trained clinicians using a goniometer to measure the angular motion of the neck in three planes. The process requires understanding complex anatomy-associated movements to collect accurate results; further unnatural movement of the routine can result in poor compliance. Hence, there is a need for a low-cost, home-based portable measurement system that engages sufferers to measure changes in neck mobility between clinical visits. Virtual Reality (VR) systems have been used in the treatment of various mental health and physiological diseases. In this study, 25 participants were tested with a novel VR system to measure and record various neck rotations in real time. The current state-of-the-art measurement system for axSpA was used for accuracy and reliability comparisons.Results showed a strong positive correlation (r = .97-.99) between data recorded from the VR and the goniometer, for all activities. While further analyses are required, results indicate that the VR application is sufficiently accurate and reliable in measuring neck movement and can be further developed as a low-cost objective measurement device for neck rehabilitation.