A multi-modal embodied robot framework was developed and evaluated to support English as a Second Language (ESL) learning in preschoolers through physical interaction and adaptive engagement. The system integrates a 4-DOF OpenManipulator-X robot with a tablet-based educational application, forming a unified instructional platform that delivers synchronized auditory, visual, and kinesthetic stimuli. Designed to improve lexical retention and motivation in early learners, the framework enables task-based interaction through pick-and-place vocabulary reinforcement, collaborative drawing, and tablet-mediated language tasks, coupled with a real-time emotion recognition module to adjust instructional cues.

An experimental design within the subject was used with 21 Korean preschool children (ages 4–8), comparing robot-assisted language learning (RALL) with traditional teacher-led language learning (TLLL) in matched tasks involving vocabulary learning, math reasoning, color categorization, and spelling recall. Each session was conducted under controlled classroom conditions and analyzed using both quantitative and qualitative metrics, including engagement frequency, task precision, and structured post-session surveys.

The results demonstrate significantly higher participation and task completion rates in the RALL condition, with vocabulary acquisition outcomes comparable to TLLL (p > 0.05). Children exhibited increased motivation and sustained interaction when guided by the robot and the application, suggesting that embodied adaptive systems can effectively support early second language learning. The study contributes validated design principles for integrating physical embodiment, affective responsiveness, and multi-modal instructional delivery in educational robotics. Implications are discussed for the scalable deployment of robot-assisted systems in preschool contexts, emphasizing child-centered interaction and developmental appropriateness within RALL environments.

This paper explores the employment implications of integrating service robots in waste management. Using the scenario technique method, 14 critical influencing factors were identified and analyzed to develop a Best-Case, Worst-Case, and Trend scenario. A SWOT analysis was used to identify implications and develop measures. The findings indicate that service robots can enhance working conditions and enable service expansion but pose risks like job displacement without proper education and reskilling. The study underscores the need for regulatory frameworks, workforce adaptation, and education to ensure socially sustainable robotic integration.

Fairness in service robotics is a complex and multidimensional concept shaped by legal, social and technical considerations. As service robots increasingly operate in personal and professional domains, questions of fairness – ranging from legal certainty and anti-discrimination to user protection and algorithmic transparency – require systematic and interdisciplinary engagement. This paper develops a working definition of fairness tailored to the domain of service robotics based on a doctrinal analysis of how fairness is understood across different fields. It identifies four key dimensions essential to fair service robotics: (i) furthering legal certainty, (ii) preventing bias and discrimination, (iii) protecting users from exploitation and (iv) ensuring transparency and accountability. The paper explores how developers, policymakers and researchers can contribute to these goals. While fairness may resist universal definition, articulating its core components offers a foundation for guiding more equitable and trustworthy human–robot interactions.

Owing to inherent flexibility, compliant actuator with physical elastic elements can implement compliant interactions between robot and environment. Nonlinear stiffness elastic actuators (NSEAs) use one motor to adjust position and stiffness, which improve compactness of variable stiffness actuators, and consider high torque/force resolution and high bandwidth. The primary challenge of designing a NSEA is designing a reliable compliant mechanism with a given torque-deflection profile. In this study, a general design method of torsional compliant mechanism through given discrete torque-deflection points was proposed, where a chain algorithm based on 2R pseudo-rigid-body model was used to describe the large deformation in elastic elements. Moreover, the theoretical model of stiffness of a compliant mechanism based on series flexible beams was developed. Based on the proposed model, the dimensional parameters of proposed compliant mechanism were designed satisfying the condition of the given torque-deflection points. Finally, simulation and physical experiment of the designed compliant mechanism through three given torque-deflection points are conducted to show the effectiveness of the proposed method. The designed compliant mechanism was tested and evaluated in the self-developed NSEA.

Thanks to recent developments in service robotics technologies, precision agriculture (PA) is becoming an increasingly prominent research field, and several studies were made to present and outline how the use of mobile robotic systems can help and improve farm production. In this paper, the integration of a custom-designed mobile base with a commercial robotic arm is presented, showing the functionality and features of the overall system for crop monitoring and sampling. To this aim, the motion planning problem is addressed, developing a tailored algorithm based on the so-called manipulability index, that treats the base and robotic arm mobility as two independent degrees of motion; also developing an open source closed-form inverse kinematics algorithm for the kinematically redundant manipulator. The presented methods and sub-system, even though strictly related to a specific mobile manipulator system, can be adapted not only to PA applications where a mobile manipulator is involved but also to the wider field of assistive robotics.

In this paper, we propose a set of robust training methods for deep reinforcement learning to transfer learning acquired in one control task to a set of previously unseen control tasks. We improve generalization in commonly used transfer learning benchmarks by a novel sample elimination technique, early stopping, and maximum entropy adversarial reinforcement learning. To generate robust policies, we use sample elimination during training via a method we call strict clipping. We apply early stopping, a method previously used in supervised learning, to deep reinforcement learning. Subsequently, we introduce maximum entropy adversarial reinforcement learning to increase the domain randomization during training for a better target task performance. Finally, we evaluate the robustness of these methods compared to previous work on simulated robots in target environments where the gravity, the morphology of the robot, and the tangential friction coefficient of the environment are altered.

We introduce the concept of social sustainability, intertwined with ecological and economic aspects, to the field of service robots and comparable automation technology. It takes a first step towards a comprehensive guideline that operationalizes and applies social sustainability. By applying this guideline to the project MURMEL we offer a concept that collects and rates social key issues to visualize their individual importance. Social sustainability is an important and often overlooked aspect of sustainable technology development which should be considered in the early development phase.

This paper deals with the problem of navigating semi-autonomous mobile robots without global localization systems in unknown environments. We propose a planning-based obstacle avoidance strategy that relies on local maps and a series of short-time coordinate frames. With this approach, simple odometry and range information are sufficient to make the robot to safely follow the user commands. Different from reactive obstacle avoidance strategies, the proposed approach chooses a good and smooth local path for the robot. The methodology is evaluated using a mobile service robot moving in an unknown corridor environment populated with obstacles and people.

This paper presents a new solution approach for managing the motion of a fleet of autonomous vehicles (AVs) in indoor factory environments. AVs are requested to serve a number of workstations (WS) (following a specified desired production plan for materials requirements) while taking into account the safe movement (collisions avoidance) in the shop floor as well as time duration and energy resources. The proposed approach is based on the Bump-Surface concept to represent the 2D environment through a single mathematical entity. The solution of the combined problem of path planning and task scheduling is searched on a higher-dimension B-surface (in our case 3D) in such a way that its inverse image into the robot environment satisfies the given objectives and constraints. Then, a modified Genetic Algorithm (GA) is used to search for a near-optimum solution. The objective of the fleet coordination consists of determining the best feasible paths for the AVs so that all the WS are served at the lowest possible cost. The efficiency of the developed method is investigated and discussed through characteristic simulated experiments concerning a variety of operating environments.

This paper introduces a novel approach to motion planning for a rapid mobile manipulator using inverted pendulum models. Our aim was to realize an actual rapid mobile manipulator with high acceleration and speed performance for an object's delivery. In our research, we developed an actual rapid mobile manipulator called KDMR-1. We proposed simple motion planning methods using a single inverted pendulum model (SIPM) and a double inverted pendulum model (DIPM), which are easily adaptable to a real-time system with only a small computational burden. The SIPM was useful for basic movement but did not provide object carrying capability. For that, a DIPM was proposed. In both models, we designed linear quadratic optimal controllers to stabilize the Zero Moment Point (ZMP). Two kinds of ZMP stabilization strategies were proposed, fixed ZMP and relaxed ZMP. Using these strategies, we realized optimal ZMP stabilizations for a real-time rapid mobile manipulator. For decoupled forward and rotational linear DIPM, we designed a centrifugal acceleration compensation model in the manner of feedback linearization. The experimental results showed high acceleration and speed performances during rapid object delivery.

Human–robot interaction is an emerging area of research where a robot may need to be working in human-populated environments. Human trajectories are generally not random and can belong to gross patterns. Knowledge about these patterns can be learned through observation. In this paper, we address the problem of a robot's social awareness by learning human motion patterns and integrating them in path planning. The gross motion patterns are learned using a novel Sampled Hidden Markov Model, which allows the integration of partial observations in dynamic model building. This model is used in the modified A* path planning algorithm to achieve socially aware trajectories. Novelty of the proposed method is that it can be used on a mobile robot for simultaneous online learning and path planning. The experiments carried out in an office environment show that the paths can be planned seamlessly, avoiding personal spaces of occupants.

In this paper, we investigate the energetics of constant height level bounding gaits in quadruped robots with asymmetric body-mass distribution along the longitudinal axis. Analytical expressions for mechanical specific resistance for two cases of bounding are derived: bounding with equal front and rear leg step lengths, and bounding with unequal front and rear leg step lengths. Specific resistance is found to be independent of mass distribution in the first case, and dependent in the second case. The quadruped robot has average nonzero acceleration/deceleration due to unsymmetric distribution of mass when front and rear leg step lengths are equal. Results show that lower body lengths, lower step lengths, and higher heights from the ground level give lower specific resistance. The effect of body-mass asymmetry is to accelerate in the first case, and to reduce specific resistance in the second case. This result provides some insight into why certain quadrupedal animals in nature evolved to have body-mass asymmetry.

This paper presents a method for designing the arm lengths of a screw drive in-pipe robot with a pathway selection mechanism to pass through bent pipes. The robot comprises a front-rotating unit (rotator), middle-steering unit, and rear-supporting unit (stator). Each unit has elastic arms, which can be elongated by springs and can be pushed against the inner wall of the pipe. The robot can travel and steer not only in straight pipes but also in bent pipes and T-branches. When the robot passes through such pipes, its mobility depends on whether the arms can touch the inner wall because the robot will lose the driving force if one of the arms cannot reach the inner wall. This is especially important in vertically positioned pipes, where the robot will fall down if it is unable to reach. In this paper, we focus on the cross-section of the bent pipes, arm length, spring stiffness, and mass of the robot to clarify the mobility of the robot in these types of pipes. A method for determining the initial arm length on the basis of simple quasi-statics is proposed and experimental verification is conducted.

The Tangent Bundle Rapidly Exploring Random Tree (TB-RRT) is an algorithm for planning robot motions on curved configuration space manifolds, in which the key idea is to construct random trees not on the manifold itself, but on tangent bundle approximations to the manifold. Curvature-based methods are developed for constructing tangent bundle approximations, and procedures for random node generation and bidirectional tree extension are developed that significantly reduce the number of projections to the manifold. Extensive numerical experiments for a wide range of planning problems demonstrate the computational advantages of the TB-RRT algorithm over existing constrained path planning algorithms.

This paper presents a vision-based obstacle avoidance design using a monocular camera onboard a mobile robot. A novel image processing procedure is developed to estimate the distance between the robot and obstacles based-on inverse perspective transformation (IPT) in an image plane. A robust image processing solution is proposed to detect and segment a drivable ground area within the camera view. The proposed method integrates robust feature matching with adaptive color segmentation for plane estimation and tracking to cope with variations in illumination and camera view. After IPT and ground region segmentation, distance measurement results are obtained similar to those of a laser range finder for mobile robot obstacle avoidance and navigation. The merit of this algorithm is that the mobile robot can have the capacity of path finding and obstacle avoidance by using a single monocular camera. Practical experimental results on a wheeled mobile robot show that the proposed imaging system successfully obtains distances of surrounding objects for reactive navigation in an indoor environment.

For reproducing the manipulation of Traditional Chinese Medicine (TCM) remedial massage and meanwhile guaranteeing safety, a 4-degree-of-freedom anthropomorphic robotic arm with integrated elastic joints is developed, and a passivity-based impedance control is used. Due to the series elasticity, integrated joints may minimize large forces that occur during accidental impacts, and, further, may offer more accurate and stable force control and a capacity for energy storage. Human expert's fingertip force curve in the process of massage therapy is acquired in vivo by a dedicated measurement device. Then three massage techniques, pressing, kneading, and plucking, are implemented by the soft arm, respectively, on torso model in vitro and on human body in vivo. Experimental results show that the developed robotic arm can effectively replicate the TCM remedial massage techniques.

This paper focuses on practical application of a mobile manipulator by presenting the development and control of a two-wheel mobile robot with two arms called a balancing service robot (BSR) designated for indoor services. The mobile manipulator requires not only robust balancing position control but also force control to interact with objects. Movements with two wheels are controlled to satisfy stable balancing control for navigation and manipulation with two arms to perform given tasks. The robot is required to deal with external forces to maintain balance. The position-based impedance force control method (the admittance control) is utilized by filtering the force with the impedance function to react to the applied force from the operator. Experimental studies of navigation control under balancing condition and interacting control with a human operator are demonstrated. Experimental results confirm that the robot has smooth reaction against the disturbance induced by the applied external force.

This paper presents the kinematics of a planar multibody vehicle which is aimed at the exploration, data collection, non-destructive testing and general autonomous navigation and operations in confined environments such as pipelines. The robot is made of several identical modules hinged by passive revolute joints. Every module is actuated with four active revolute joints and can be regarded as a parallel mechanism on a mobile platform. The proposed kinematics allows to overcome the nonholonomic kinematic constraint, which characterizes typical wheeled robots, resulting into a higher number of degrees of freedom and therefore augmented actuation inputs. Singularities in the kinematics of the modules are analytically identified. We present the dimensional synthesis of the length of the arms obtained as the solution of an optimization problem with respect to a suitable dexterity index. Simulation results illustrate a kinematic control path following inside pipes. Critical scenarios such as 135° elbows and concentric restriction are explored. Path following shows the kinematic capability of deployment of the robot for autonomous operations in pipelines, with feedback implemented by on-board sensors.

This paper summarizes the recent standardization activities in the field of robotics by ISO (International Organization for Standardization), IEC (International Electrotechnical Commission), OMG (Object Management Group), and other organizations. While the standards in industrial robots have been mainly developed by ISO, the standards on the emerging service robots are initiated by many organizations. One of the goals of this paper is to coordinate the efforts among these groups so that more effective standardization activity can be executed. Standardization in the emerging service robots will eventually promote the proliferation of service robot markets in the near future.

In this paper, we present the work related to the application of a visual odometry approach to estimate the location of mobile robots operating in off-road conditions. The visual odometry approach is based on template matching, which deals with estimating the robot displacement through a matching process between two consecutive images. Standard visual odometry has been improved using visual compass method for orientation estimation. For this purpose, two consumer-grade monocular cameras have been employed. One camera is pointing at the ground under the robot, and the other is looking at the surrounding environment. Comparisons with popular localization approaches, through physical experiments in off-road conditions, have shown the satisfactory behavior of the proposed strategy.