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The 7th International Workshop on Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI) seeks to bring together researchers from human-robot interaction (HRI), robotics, and mixed reality (MR) to address the challenges related to mixed reality interactions between humans and robots. Key topics include the development of robots capable of interacting with humans in mixed reali
Impactful applications such as materials discovery, hardware design, neural architecture search, or portfolio optimization require optimizing high-dimensional black-box functions with mixed and combinatorial input spaces. While Bayesian optimization has recently made significant progress in solving such problems, an in-depth analysis reveals that the current state-of-the-art methods are not reliab
Channel sounding is essential for the development of radio systems. One flexible strategy is the switched-array-based channel sounding, where antenna elements are activated at different time instants to measure the channel spatial characteristics. Although its hardware complexity is decreased due to fewer radio-frequency (RF) chains, sequentially switching the antenna elements can result in aliasi
Electric power systems are undergoing huge changes due to the shift from conventional power production to more renewable-based generation like solar and wind. This is primarily driven by the need to mitigate climate change by reducing CO2 emissions. The shift to more generation from solar and wind will affect the dynamical behaviour of power systems, and consequently how they should be controlled.
The advent of 6G wireless communication marks a transformative era in technological connectivity, bringing forth challenges and opportunities alike. This paper unveils an innovative, open-source simulator, meticulously crafted for cell-free 6G wireless networks. This simulator is not just a tool but a gateway to the future, blending cutting-edge channel models with the simulation of both physical
This paper proposes a motion control scheme for robots operating in a dynamic environment with concave obstacles. A Model Predictive Controller (MPC) is constructed to drive the robot towards a goal position while ensuring collision avoidance without direct use of obstacle information in the optimization problem. This is achieved by guaranteeing tracking performance of an appropriately designed re
In this letter, we give an upper bound on the deviation from H-infinity optimality of a class of controllers as a function of the deviation from symmetry in the state matrix. We further suggest a scalar measure of symmetry which is shown to be directly relevant for estimating nearness to optimality. In connection to this, we give a simple analytical solution to a class of Lyapunov equations for tw
Contact-rich manipulation tasks remain a hard problem in robotics that requires interaction with unstructured environments. Reinforcement Learning (RL) is one potential solution to such problems, as it has been successfully demonstrated on complex continuous control tasks. Nevertheless, current state-of-the-art methods require policy training in simulation to prevent undesired behavior and later d
Reconstruction of movement control properties of the brain could result in many potential advantages for application in robotics. However, a hampering factor so far has been the lack of knowledge of the structure and function of brain circuitry in vivo during movement control. Much more detailed information has recently become available for the area of the cerebellum that controls arm-hand movemen
In this paper, we apply an optimal Linear Quadratic (LQ) controller, which has an inherent structure that allows for a distributed implementation, to an irrigation network. The network consists of a water reservoir and connected water canals. The goal is to keep the levels close to the set-points when farmers take out water. The LQ controller is designed using a first-order approximation of the ca
Can simple systems be regulated by simple controllers? Engineering experience indicates that this is often the case. Certainly, the overwhelming majority of industrially deployed controllers are proportional-integral-derivative (PID) controllers, many of which are tuned without detailed models. This is often in disappointing contrast with the controllers produced using model-based approaches. This
In this paper, we present a global solution to a nonlinear H-infinity optimal control problem for control-affine systems with an associated potential function. We also give a closed-form expression for an optimal controller and demonstrate its potential for sparsity. This paper thus advances a recent result which considers the same problem restricted to systems with symmetric state matrix and nonl
The aim of this study was to establish traceable number concentration measurements of airborne particles beyond 10 μm in particle size. To this end, the primary standards for particle number concentration at the National Metrology Institutes of Switzerland and Japan were further developed to extend their measurement capabilities. Details on the upgraded setup are provided. An inter-comparison of t
In modern industrial settings with small batch sizes it should be easy to set up a robot system for a new task. Strategies exist, e.g. the use of skills, but when it comes to handling forces and torques, these systems often fall short. We introduce an approach that provides a combination of task-level planning with targeted learning of scenario-specific parameters for skill-based systems. We propo
When we speak, we typically also gesture. Gestures are an integral part of language use in both production and comprehension. They are therefore also vital in second language (L2), foreign language (FL), and bilingualism studies since they can open new windows on cognitive processes, interaction practices, and learning. This entry discusses methods and tools for studying second language users' and
A well-balanced exploration-exploitation trade-off is crucial for successful acquisition functions in Bayesian optimization. However, there is a lack of quantitative measures for exploration, making it difficult to analyze and compare different acquisition functions. This work introduces two novel approaches - observation traveling salesman distance and observation entropy - to quantify the explor
Bayesian optimization (BO ) is an effective method for optimizing expensive-to-evaluate black-box functions. While high-dimensional problems can be particularly challenging, due to the multitude of parameter choices and the potentially high number of data points required to fit the model, this limitation can be addressed if the problem satisfies simplifying assumptions. Axis-aligned subspace appro
Many problems in computer vision can be formulated as geometric estimation problems, i.e. given a collection of measurements (e.g. point correspondences) we wish to fit a model (e.g. an essential matrix) that agrees with our observations. This necessitates some measure of how much an observation 'agrees' with a given model. A natural choice is to consider the smallest perturbation that makes the o
We summarize the theory and implementation of an open source solver for rotationally symmetric scatterers. The purpose of the code is to compute reference solutions for electrically large radome problems. The rotational symmetry decouples the azimuthal modes, enabling the three-dimensional problem to be broken down into a sequence of decoupled two-dimensional problems which require much less memor
