Reviewerly/exHarmony huggingface dataset:
Trusted Execution of Periodic Tasks for Embedded Systems*

Systems that interact with the environment around them generally run some periodic tasks. This class of systems include, among others, embedded control systems. Embedded controllers have been proven vulnerable to various security attacks, including attacks that alter sensor and actuator data and attacks that disrupt the calculation of the control signals. In this paper, we propose, and implement, a mechanism to execute a periodic task and its communication interfaces in a trusted execution environment. This allows us to execute an isolated controller, thus offering higher security guarantees. We analyse the overhead of switching between the regular (possibly compromised) execution environment and the trusted execution environment and quantify the effect of this defence mechanism on the control performance.
Synthesis of an Obfuscation Policy that Guarantees Utility Satisfying a New Privacy Criterion

Nowadays, with the advancement of technology, several systems are connected through computer networks, which makes it necessary to develop techniques capable of guaranteeing the privacy and utility of the transmitted information. In this work, we consider the synthesis of an obfuscation policy of Discrete Event Systems that guarantees utility satisfying a new privacy criterion. The system is modeled as a labeled transition system with secret states. The first goal is to keep the information generated by the system useful so that legitimate users can use it for decision making. At the same time, the second goal is to hide critical information about the secret states of the system from attackers. However, unlike previous approaches presented in the literature, we consider the case in which it is possible for the obfuscator to report the secret states if the actual state of the system is in a safe region. Thus, this approach increases the number of systems that can be obfuscated. The software SynthSMV is used to perform the obfuscator synthesis.
A Unified Approach to Differentially Private Bayes Point Estimation

Parameter estimation in statistics and system identification relies on data that may contain sensitive information. To protect this sensitive information, the notion of differential privacy (DP) has been proposed, which enforces confidentiality by introducing randomization in the estimates. Standard algorithms for differentially private estimation are based on adding an appropriate amount of noise to the output of a traditional point estimation method. This leads to an accuracy-privacy trade off, as adding more noise reduces the accuracy while increasing privacy. In this paper, we propose a new Unified Bayes Private Point (UBaPP) approach to Bayes point estimation of the unknown parameters of a data generating mechanism under a DP constraint, that achieves a better accuracy-privacy trade off than traditional approaches. We verify the performance of our approach on a simple numerical example.
Efficient Computation of Inner Approximations of Reachable Sets for a Verified Motion Planning Concept

Verification of control systems often involves the computation of reachable sets. In this context, reachable sets are used to verify that a system is capable of reaching a desired set of states or that it will not reach a set of undesired states. This contribution presents a novel method for computing convex inner approximations of reachable sets for non-linear input-affine systems with constrained inputs. The inner approximation is obtained using the reachable set of the linearized system that is shrunk by a factor depending on the linearization errors. The proposed method is integrated into a motion planning concept for mobile robots. In this way, the motion planning concept guarantees, that motions that are planned based on a simplified model of a robot can be reached by an accurate model of the robot. The application example demonstrates the computational efficiency of the proposed method as it is utilized to compute inner approximated reachable sets of a system that consists of 18 states and features eight inputs.
Two-Step Online Trajectory Planning of a Quadcopter in Indoor Environments with Obstacles

This paper presents a two-step algorithm for online trajectory planning in indoor environments with unknown obstacles. In the first step, sampling-based path planning techniques such as the optimal Rapidly exploring Random Tree (RRT*) algorithm and the Line-of-Sight (LOS) algorithm are employed to generate a collision-free path consisting of multiple waypoints. Then, in the second step, constrained quadratic programming is utilized to compute a smooth trajectory that passes through all computed waypoints. The main contribution of this work is the development of a flexible trajectory planning framework that can detect changes in the environment, such as new obstacles, and compute alternative trajectories in real time. The proposed algorithm actively considers all changes in the environment and performs the replanning process only on waypoints that are occupied by new obstacles. This helps to reduce the computation time and realize the proposed approach in real time. The feasibility of the proposed algorithm is evaluated using the Intel Aero Ready-to-Fly (RTF) quadcopter in simulation and in a real-world experiment.
Single-state weighted particle filter with application to Earth Observation missions

To push the boundaries of autonomy in space, the spacecraft must rely on its own sensors to achieve positioning and environmental perception. In this context, the key problem of autonomous navigation is the nonlinear state estimation of the spacecraft in a dynamic 3D environment. In this paper, we propose a new approach based on a single-state sub-partitioning of the state vector and a partial updating of the vector of weights according to the specific information provided by each sensor. In this way, we avoid to lose information in the resampling phase thanks to a parallelization approach. The proposed method has been applied to an Earth observation mission and the efficacy of the proposed approach is demonstrated with a numerical example using a high-fidelity orbital simulator.
Learning Locally, Communicating Globally: Reinforcement Learning of Multi-robot Task Allocation for Cooperative Transport

We consider task allocation for multi-object transport using a multi-robot system, in which each robot selects one object among multiple objects with different and unknown weights. The existing centralized methods assume the number of robots and tasks to be fixed, which is inapplicable to scenarios that differ from the learning environment. Meanwhile, the existing distributed methods limit the minimum number of robots and tasks to a constant value, making them applicable to various numbers of robots and tasks. However, they cannot transport an object whose weight exceeds the load capacity of robots observing the object. To make it applicable to various numbers of robots and objects with different and unknown weights, we propose a framework using multi-agent reinforcement learning for task allocation. First, we introduce a structured policy model consisting of 1) predesigned dynamic task priorities with global communication and 2) a neural network-based distributed policy model that determines the timing for coordination. The distributed policy builds consensus on the high-priority object under local observations and selects cooperative or independent actions. Then, the policy is optimized by multi-agent reinforcement learning through trial and error. This structured policy of local learning and global communication makes our framework applicable to various numbers of robots and objects with different and unknown weights, as demonstrated by simulations.
Robust Reinforcement Learning Based Visual Servoing with Convolutional Features

Image-based visual servoing poses a significant challenge for robotic systems, as it involves detecting the object and controlling the robot arm based on image feedback. These tasks are further complicated by various interferences such as changes in ambient lighting, distractions, and background clutter. Recent research suggests that reinforcement learning is a promising approach to learning efficient control policies for such tasks. In this paper, we propose a data-driven approach for closed-loop visual servoing based on a reinforcement learning algorithm that does not require any prior knowledge of the task object or intrinsic camera parameters. Our method utilizes a convolutional neural network for object detection and a servoing strategy that enables the robot to determine the relative camera motion and position the camera at the desired pose. Our experimental results demonstrate that the proposed approach successfully steers the camera using only a single template image of the task object.
Efficient sample selection for safe learning*

Ensuring safety in optimization is challenging if the underlying functional forms of either the constraints or the objective function are unknown. The challenge can be addressed by using Gaussian processes to provide confidence intervals used to find solutions that can be considered safe. To iteratively find a trade-off between finding the solution and ensuring safety, the SafeOpt algorithm builds on algorithms using only the upper bounds (UCB-type algorithms) by performing an exhaustive search on the entire search space to find a safe iterate. That approach can quickly become computationally expensive. We reformulate the exhaustive search as a series of optimization problems to find the next recommended points. We show that the proposed reformulation allows using a wide range of available optimization solvers, such as derivative-free methods. We show that by exploiting the properties of the solver, we enable the introduction of new stopping criteria into safe learning methods and increase flexibility in trading off solver accuracy and computational time. The results from a non-convex optimization problem and an application for controller tuning confirm the flexibility and the performance of the proposed reformulation.
Image Selection Method from Image Sequence to Improve Computational Efficiency of 3D Reconstruction: Application of Robust Threshold Based on Multimodal Test to Images

This paper describes the image selection method by multimodal detection for improving the computational efficiency of three-dimensional (3D) reconstruction based on images in a time-series. To decrease the calculation time of the 3D reconstruction, an adequate selection from the images is required. For this reason, we introduced multimodal detection by a statistical test on the image selection process, and then applied it to soundness evaluation of the displacements based on the optical flow between images acquired by a camera. The results suggest that suitable images can be extracted from the images in a time-series for decreasing the calculation time of the 3D reconstruction. Therefore, the suitable images selected by the proposed method contributed to efficiently performing the 3D reconstruction.
A Bayesian Filtering Method for Wiener State-Space Systems Utilizing a Piece-wise Linear Approximation

In this paper, we develop a filtering algorithm for Wiener systems written in state-space form which considers correlated noise sources. The output non-linearity is approximated by using a piece-wise linear function. The probability function of the output signal conditioned to the system state is written as a Gaussian mixture distribution. A Gaussian sum filter algorithm to obtain the a posteriori probability density function of the state given the current and past output is developed. The associated statistics of the system state are obtained. The benefits of our proposal are illustrated via numerical simulations.
Motion Planning with Specified Terminal Velocity Direction of Fixed-Wing UAVs in 3D*

This paper focuses on the motion planning problem of fixed-wing UAVs in 3D whose velocities are restricted by the nonholonomic constraints. Different from common planning which only requires the target position, we additionally bring in the velocity direction in the sense that the fixed-wing UAV should reach the desired position with the specified velocity direction at the terminal time. Besides, the fixed-wing UAV is modelled as a rigid body with six degrees of freedom rather than a particle agent. The main idea of the proposed motion planning algorithm is to design a guiding vector field over the workspace. By following the vector field, the fixed-wing can be navigated to the destination with the desired velocity direction. Simulations are conducted to verify effectiveness of the theoretical results.
Neural motion planning in dynamic environments*

Motion planning is a mature field within robotics with many successful solutions. Despite this, current state-of-the-art planners are still computationally heavy. To address this, recent work have employed ideas from machine learning, which have drastically reduced the computational cost once a planner has been trained. It is mainly static environments that have been studied in this way. We continue along the same research direction but expand the problem to include dynamic environments, hence increasing the difficulty of the problem. Analogously to previous work, we use imitation learning, where a planning policy is learnt from an expert planner in a supervised manner. Our main contribution is a planner mimicking an expert that considers the future movement of all the obstacles in the environment, which is key in order to learn a successful policy in dynamic environments. We illustrate this by evaluating our approach in a dynamic environment and by comparing our planner with a conventional planner that re-plans at every iteration, which is a common approach in dynamic motion planning. We observe that our approach yields a higher success rate, while also taking less time and accumulating less distance to reach the goal.
Zero-Shot Sim2Real Transfer of Deep Reinforcement Learning Controller for Tower Crane System

Control of nonlinear systems is a challenging task that often requires linearization, which limits the operating envelope. Moreover, designing a controller for such nonlinear systems requires complex tuning rules and expert system knowledge. Deep Reinforcement Learning (DRL) has been used as a controller for nonlinear systems. However, its applicability is hindered by the Sim2Real gap, an issue where the DRL agent trained in simulation does not transfer to reality. In this work, we propose using DRL for the control of nonlinear systems across the Sim2Real gap by utilizing a detailed dynamic model of the nonlinear system. To demonstrate the proposed methodology, we use a tower crane as the nonlinear system of interest. The control policy of the DRL agent is learned rather than explicitly programmed. To highlight the effectiveness of the proposed DRL-based controller, the results are compared with the PI controller. The experimental results presented demonstrate the successful Sim2Real transfer and effectiveness of the proposed approach.
Shape Servoing of Deformable Objects using Adaptive Deformation Model Estimation

In this paper, we propose an adaptive shape servoing method to deform a soft object into a desired 3-D shape. The high dimensional representation and the unknown deformation properties of the soft object pose a challenge to actively manipulate its shape. To address this issue, we develop a method to compute the deformation Jacobian matrix in real-time. The Jacobian is estimated using a set of basis functions and its corresponding parameters to capture the dynamics of the system and relate the applied input motion to changes in the soft object's shape. An integral concurrent learning (ICL) based adaptive update law is derived using Lyapunov analysis to estimate the deformation parameters and prove its convergence. A physics-based simulation is used to validate the proposed method and controller by performing manipulation tasks with different desired configurations. The performance is compared with a standard gradient update law to demonstrate the accuracy and robustness of our approach.
Innovation Process Modelling for Human-Centred Cultural Heritage Spaces: Developing a Business Model Canvas for Smart Digital Immersion

The cultural heritage sector offers an opportunity to gain an insight into the customs and traditions of previous generations and acquire an understanding of the progression of society. This discipline is important for an individual to maintain a sense of identity in the world and attain knowledge of important historical events. Cultural heritage items that are reflective of a group within society can be presented in the form of artefacts or works of art in a setting such as a museum or art gallery. Innovation is key when implementing technological solutions to continuously look towards new methods that will efficiently showcase all items as desired and preserve this content for future generations. A museum for example generally exhibits tangible and intangible heritage with the difference being the ability to physically interact with the items. Emergent digital technologies offer an opportunity to progress from the traditional user experience to an immersive and personalised control system. This opportunity has not yet been fully explored within the cultural heritage sector where Artificial Intelligence (AI) and the Internet of Things (IOT) can be utilised in tandem to achieve this objective. The Business Model Canvas (BMC) was created to mitigate uncertainty regarding the operation of commercial ideas through enhanced visualisation. This paper presents a populated BMC for smart digital immersion in cultural heritage spaces and addresses the significance of an alignment with the human-centred philosophy when incorporating immersive technologies in the user journey.
ADArduPilot: an ArduPilot compatible adaptive autopilot⋆

We release an autopilot that is fully compatible with all the vehicle types the ArduPilot suite was designed for (copters, multi-copters, fixed-wing aircraft, rovers, surface vessels, etc.). The main feature of this autopilot is that adaptation capabilities are embedded without modifying the original ArduPilot architecture, hence the name ADArduPilot. We test the proposed autopilot in several scenarios requiring adaptation (different payloads, different vehicle mass, environmental disturbances), showing that the proposed adaptation mechanism can consistently deliver improved performance in terms of tracking error and control effort.
Critical Assessment of Mammography Accuracy

Mammography is currently considered the gold standard for breast screening, despite painful breast compression, invasive radiation exposure and excessive infrastructure and personnel requirements, contributing to its inequity. Furthermore, sensitivity and specificity of mammog-raphy are often overstated by studies utilising flawed methodology. This paper critically reviews the literature reporting on methods used to calculate diagnostic accuracy of mammography. Values for sensitivity, specificity and receiver operator characteristic (ROC) curve area (AUC) are presented by averaging results from studies inclusive of a comparison to another breast screening modality. The result is mammography sensitivity of 60%, specificity of 80%, AUC of 0.73. These values should be used when assessing diagnostic performance of other breast screening technologies.
Implementation of Autonomous Supply Chains for Digital Twinning: a Multi-Agent Approach

Trade disruptions, the pandemic, and the Ukraine war over the past years have adversely affected global supply chains, revealing their vulnerability. Autonomous supply chains are an emerging topic that has gained attention in industry and academia as a means of increasing their monitoring and robustness. While many theoretical frameworks exist, there is only sparse work to facilitate generalisable technical implementation. We address this gap by investigating multi-agent system approaches for implementing autonomous supply chains, presenting an autonomous economic agent-based technical framework. We illustrate this framework with a prototype, studied in a perishable food supply chain scenario, and discuss possible extensions.
A High-Speed Method of Segmenting Human Body Motions with Regular Time Interval Sensor Data Based on Gaussian Process Hidden Semi-Markov Model

Real-time action detection and feedback systems are needed to reduce the load on assembly line workers. Segmenting motion based on a Gaussian process hidden semi-Markov model (GP-HSMM) is suitable for detecting workers' actions because an assembly line involves repeated movements. However, GP-HSMM has bottlenecks in calculating inverse covariance matrices and forward probabilities. To remove those bottlenecks, we first converted block covariance matrices into circulant matrices by combining sensor data at the same time step with a distributional kernel so that we can solve them with an FFT. Secondly, we parallelized forward probabilities by shifting rows of the log-likelihood matrix, which is calculated from all of the combinations between the signal patterns generated from the Gaussian process and observed data, and summing up rows and columns incrementally. In an experiment with 2593 × 11 electromyography and inertial measurement unit time series data, our algorithm was 495 times faster than conventional methods.
A Spectral Method of Moments for Hierarchical Imitation Learning

Recent empirical success has led to a rise in popularity of the options framework for Hierarchical Reinforcement Learning (HRL). This framework tackles the scalability problem in Reinforcement Learning (RL) by introducing a layer of abstraction (i.e. high-level options) over the (low-level) decision process. Hierarchical Imitation Learning (HIL) is the problem of learning low-level and high-level policies within HRL from expert demonstrations consisting only of the low-level actions and states, with the high-level options being hidden (or latent). Due to the latent options, recent work on HIL has focused on the development of Expectation-Maximization (EM) algorithms inspired by approaches such as the celebrated Baum-Welch algorithm for hidden Markov models (HMMs). In this work, we take a different approach and derive a new HIL framework inspired by the spectral method of moments for HMMs. The method of moments offers global and consistent convergence under mild regulatory conditions, whilst only requiring one sweep through the data set of state and action pairs, giving it a competitive run time.