Parameter Estimation and Q‐Learning Based Adaptive Sensitivity Amplification Control for Cable‐Driven Lower‐Limb Exoskeleton

Enhancing Humans Trust in Robots through Explanations

Development of robotics and growing use of robots in many areas of the economy and social life require research which will identify problems related to the use of modern technologies and the development of human-robot interaction (HRI, Human-Robot Interaction). Robotics, autonomous systems and artificial intelligence also create a number of new legal and ethical challenges. Research in this area has been expanding in recent years, but there are still issues that require in-depth scientific exploration. The objective of this paper is to systematize knowledge about interactions between people and robots, with particular emphasis on interactions in the work environment, and to identify research gaps in the analysed problem area. The following research questions were formulated: 1) What determines people's attitudes towards robots? 2) What are the key challenges in human-robot communication? 3) What ethical challenges determine human-robot interaction? In the studies the the method of narrative literature review has been used. The latest scientific publications on the researched issue were analysed. The results of the study indicate that the main factors which determine people's attitudes towards robots are apart from demographic variables and experiences gained by people from contacts with robots, the perceived usefulness of using robots, the image of robots created in the mass media and religiousness. The key challenges in human-robot interaction apart from effective communication is the reflection of emotions. The research results enable the identification of further directions of scientific research on human-robot interaction. They can also enrich practitioner’s perspective in the studied area.

In structural equation modeling (SEM), parameter estimates are typically computed by the Fisher-scoring algorithm, which often has difficulty in obtaining converged solutions. Even for simulated data with a correctly specified model, non-converged replications have been repeatedly reported in the literature. In particular, in Monte Carlo studies it has been found that larger factor loadings or smaller error variances in a confirmatory factor model correspond to a higher rate of convergence. However, studies of a ridge method in SEM indicate that adding a diagonal matrix to the sample covariance matrix also increases the rate of convergence for the Fisher-scoring algorithm. This article addresses these two seemingly contradictory phenomena. Using statistical and numerical analyses, the article clarifies why both approaches increase the rate of convergence in SEM. Monte Carlo results confirm the analytical results. Recommendations are provided on how to increase both the speed and rate of convergence in parameter estimation.

Human–robot interaction (HRI) in human social environments (HSEs) poses unique challenges for robot perception systems, which must combine asynchronous, heterogeneous data streams in real time. Multimodal perception systems are well-suited for HRI in HSEs and can provide more rich, robust interaction for robots operating among humans. In this article, we provide an overview of multimodal perception systems being used in HSEs, which is intended to be an introduction to the topic and summary of relevant trends, techniques, resources, challenges, and terminology. We surveyed 15 peer-reviewed robotics and HRI publications over the past 10+ years, providing details about the data acquisition, processing, and fusion techniques used in 65 multimodal perception systems across various HRI domains. Our survey provides information about hardware, software, datasets, and methods currently available for HRI perception research, as well as how these perception systems are being applied in HSEs. Based on the survey, we summarize trends, challenges, and limitations of multimodal human perception systems for robots, then identify resources for researchers and developers and propose future research areas to advance the field.

Social navigation framework for assistive robots in human inhabited unknown environments

Adaptive control has been developed for decades, and now it has become a rigorous and mature discipline which mainly focuses on dealing parametric uncertainties in control systems, especially linear parametric systems. Nonparametric uncertainties were seldom studied or addressed in the literature of adaptive control until new areas on exploring limitations and capability of feedback control emerged in recent years. Comparing with the approach of robust control to deal with parametric or nonparametric uncertainties, the approach of adaptive control can deal with relatively larger uncertainties and gain more flexibility to fit the unknown plant because adaptive control usually involves adaptive estimation algorithms which play role of “learning” in some sense. This chapter will introduce a new challenging topic on dealing with both parametric and nonparametric internal uncertainties in the same system. The existence of both two kinds of uncertainties makes it very difficult or even impossible to apply the traditional recursive identification algorithms which are designed for parametric systems. We will discuss by examples why conventional adaptive estimation and hence conventional adaptive control cannot be applied directly to deal with combination of parametric and nonparametric uncertainties. And we will also introduce basic ideas to handle the difficulties involved in the adaptive estimation problem for the system with combination of parametric and nonparametric uncertainties. Especially, we will propose and discuss a novel class of adaptive estimators, i.e. information-concentration (IC) estimators. This area is still in its infant stage, and more efforts are expected in the future for gainning comprehensive understanding to resolve challenging difficulties. Furthermore, we will give two concrete examples of semi-parametric adaptive control to demonstrate the ideas and the principles to deal with both parametric and nonparametric uncertainties in the plant. (1) In the first example, a simple first-order discrete-time nonlinear system with both kinds of internal uncertainties is investigated, where the uncertainty of non-parametric part is characterized by a Lipschitz constant L, and the nonlinearity of parametric part is characterized by an exponent index b. In this example, based on the idea of the IC estimator, we construct a unified adaptive controller in both cases of b = 1 and

Natural human–robot interaction (HRI) in complex and unpredictable environments is important with many potential applications. While vision-based HRI has been thoroughly investigated, robot hearing and audio-based HRI are emerging research topics in robotics. In typical real-world scenarios, humans are at some distance from the robot and, hence, the sensory (microphone) data are strongly impaired by background noise, reverberations and competing auditory sources. In this context, the detection and localization of speakers plays a key role that enables several tasks, such as improving the signal-to-noise ratio for speech recognition, speaker recognition, speaker tracking, etc. In this paper we address the problem of how to detect and localize people that are both seen and heard. We introduce a hybrid deterministic/probabilistic model. The deterministic component allows us to map 3D visual data onto a 1D auditory space. The probabilistic component of the model enables the visual features to guide the grouping of the auditory features in order to form audiovisual (AV) objects. The proposed model and the associated algorithms are implemented in real-time (17 FPS) using a stereoscopic camera pair and two microphones embedded into the head of the humanoid robot NAO. We perform experiments with (i) synthetic data, (ii) publicly available data gathered with an audiovisual robotic head, and (iii) data acquired using the NAO robot. The results validate the approach and are an encouragement to investigate how vision and hearing could be further combined for robust HRI.

This study develops a comprehensive robotic system, termed the robot cognitive system, for complex environments, integrating three models: the engagement model, the intention model, and the human-robot interaction (HRI) model. The system aims to enhance the naturalness and comfort of HRI by enabling robots to detect human behaviors, intentions, and emotions accurately. A novel dual-arm-hand mobile robot, Mobi, was designed to demonstrate the system's efficacy. The engagement model utilizes eye gaze, head pose, and action recognition to determine the suitable moment for interaction initiation, addressing potential eye contact anxiety. The intention model employs sentiment analysis and emotion classification to infer the interactor's intentions. The HRI model, integrated with Google Dialogflow, facilitates appropriate robot responses based on user feedback. The system's performance was validated in a retail environment scenario, demonstrating its potential to improve the user experience in HRIs.

PurposeThe purpose of this paper is to study the adaptability of the tracked robot in complex working environment. It proposes an angle-changeable tracked robot with human–robot interaction in unstructured environment. The study aims to present the mechanical structure and human–robot interaction control system of the tracked robot and analyze the static stability of the robot working in three terrains, i.e. rugged terrain, sloped terrain and stairs.Design/methodology/approachThe paper presents the mechanical structure and human–robot interaction control system of the tracked robot. To prevent the detachment of the tracks during obstacle navigation, a new type of passively adaptive device based on the relationship between the track’s variable angle and the forces is presented. Then three types of rough terrain are chosen to analyze the static stability of the tracked robot, i.e. rugged terrain, sloped terrain and stairs.FindingsThis paper provides the design method of the tracked robot. Owing to its appropriate dimensions, good mass distribution and limited velocity, the tracked robot remains stable on the complex terrains. The experimental results verify the effectiveness of the design method.Originality/valueThe theoretical analysis of this paper provides basic reference for the structural design of tracked robots.

Adaptive control is essential for enabling mobile robots to operate autonomously in dynamic and unpredictable environments. Despite extensive research, existing approaches remain fragmented across methodologies, application domains, and evaluation criteria. This review presents a unified taxonomy of five core adaptive control strategies: Model Reference Adaptive Control (MRAC), Fuzzy Logic Adaptive Control (FLAC), Neural Network Adaptive Control (NNAC), Reinforcement Learning-Based Adaptive Control (RLAC), and hybrid approaches. These strategies are examined across four primary robotic domains: autonomous navigation, mobile manipulation, multi-robot coordination, and human-in-the-loop systems. Unlike prior reviews that focus on specific techniques or limited domains, this work provides a comprehensive synthesis of the strengths and limitations of each strategy with respect to adaptability, robustness, computational efficiency, and real-world deployment. Hybrid methods exhibit strong potential due to their flexibility but often introduce increased design complexity and implementation challenges. Key open issues include the lack of standardized performance metrics, limited validation on physical hardware, and restricted generalizability to unstructured environments. Addressing these challenges requires interdisciplinary collaboration across robotics, control, artificial intelligence, embedded systems, and human–robot interaction. By linking control strategies to practical applications, this review supports the development of adaptive robotic systems that are scalable, explainable, energy-efficient, and responsive to human behavior.

We establish minimax lower bounds and maximum likelihood convergence rates of parameter estimation for mean-covariance multivariate Gaussian mixtures, shape-rate Gamma mixtures and some variants of finite mixture models, including the setting where the number of mixing components is bounded but unknown. These models belong to what we call “weakly identifiable” classes, which exhibit specific interactions among mixing parameters driven by the algebraic structures of the class of kernel densities and their partial derivatives. Accordingly, both the minimax bounds and the maximum likelihood parameter estimation rates in these models, obtained under some compactness conditions on the parameter space, are shown to be typically much slower than the usual $n^{-1/2}$ or $n^{-1/4}$ rates of convergence.

The convergence rate of an LMS adaptive FIR filter to an unknown stationary channel may be influenced by the filter parameter dimension as well as by the input signal's characteristics. This dimension influence may be of importance in applications, such as adaptive acoustic echo cancellation, in which the unknown channel is typically modeled as a "long" FIR filter. The paper includes the development and proposal of a novel measure of the expected convergence rate of the LMS/FIR filter followed by analysis of this convergence rate measure. The analysis indicates that unless the input signal is white, the expected convergence rate decreases with increasing dimension down to a limiting value, which is determined by the input signal's autocorrelation level.

Bickel and Ritov suggested an optimal estimator for the integral of the square of the kth derivative of a density when the unknown density belongs to a Lipschitz class of a given order $\beta$. In this context optimality means that the estimate is asymptotically efficient, that is, it has the best constant and rate of risk convergence, whenever $\beta > 2k + 1/4$, and it is rate optimal otherwise. The suggested optimal estimator crucially depends on the value of $\beta$ which is obviously unknown. Bickel and Ritov conjectured that the method of cross validation leads to a corresponding adaptive estimator which has the same optimal statistical properties as the optimal estimator based on prior knowledge of $\beta$. We show for probability densities supported over a finite interval that when $\beta > 2k + 1/4$ adaptation is not necessary for the construction of an asymptotically efficient estimator. On the other hand, it is not possible to construct an adaptive estimator which has the same rate of convergence as the optimal nonadaptive estimator as soon as $k < \beta \leq 2k + 1/4$.

Nonlinear modeling and identification of proton exchange membrane fuel cell (PEMFC)

Real Data Testing of Model Parameter Estimation Methods for Macroscopic Traffic Flow Model