Search Results (43)

This study focuses on the construction sequence of three complex and atypical Scythian kurgans at the Vodoslavka burial ground in the Northern Sivash region, which incorporate several unique structural and ritual elements. One of the most striking features is the layer of mud applied to the ground surface prior to mound construction, which, in several cases, formed anthropomorphic outlines. Funerary feasting, which took place both before and during the burial ceremony, was just one of the other features. So too was the deliberate shaping of soil removed from the central grave into a spherical segment, and the ritual activity associated with this prepared spoil heap. Although the mounds’ preserved height is relatively modest (originally about 3–5 m), their internal organisation and the composition of the grave goods suggest that they were used for burying individuals of high status within Scythian society. The cemetery’s proximity to major salt lakes suggests that the local elite’s affluence may have been linked to their control over this vital resource. The architectural and depositional features of the kurgans can be interpreted as elements of a ritual system designed to ensure the deceased’s proper transition to the afterlife. The design of the burial chambers and the richness of the grave goods reflect a concern for the conditions of existence in the afterlife, while the associated manipulations of the sub-mound space and mound deposits, prepared surfaces, deliberately shaped spoil heaps, and related ritual practices can be understood as material markers and procedures intended to secure the successful passage of the deceased to the afterlife. Full article

In this paper, an engineering model (EM) of a multi-joint space gripper for on-orbit servicing (OOS) is proposed. OOS missions demand robotic systems capable of reliable physical interactions under dynamic uncertainties and harsh space environments. While prior space-qualified grippers have demonstrated dexterous manipulation through anthropomorphic, high-DoF configurations, this work adopts a design direction widely established in industrial applications: a three-finger, lower-DoF configuration that balances grasp versatility, structural simplicity, and system integration for OOS missions. The developed gripper features a tendon-driven mechanism with a structural design optimized for space-environment compatibility and mechanical compliance. The kinematic characteristics of the mechanism are analyzed, while workspace and manipulability analyses are conducted to evaluate its operational limits. To verify the functional feasibility of the proposed design, representative grasping experiments were performed using a fabricated EM. The mechanical reliability and grasping performance were evaluated through a series of empirical experiments. The results indicate that the proposed design achieves a practical balance among grasp versatility, structural simplicity, and system integration for OOS missions, with a shielding-oriented structural configuration adopted as a design baseline. Its functional feasibility is supported by kinematic analysis, repeatability verification, and grasping experiments. This study provides a basis for the design and evaluation of three-finger robotic grippers in future OOS missions. Full article

Industrial robots are increasingly deployed in human-centered settings, where appearance and motion critically shape worker trust and acceptance. This study employed a 2 × 2 factorial design manipulating robot appearance (Sleek vs. Industrial) and motion (Adaptive vs. Rigid) to examine effects on perceived lifelikeness, intelligence, engagement, trust, and predictability. Participants rated each measure using Likert scales, and data were analyzed using descriptive statistics, two-way ANOVA, and Pearson correlations. Results revealed significant main effects of appearance and movement across multiple perceptual dimensions, with a significant interaction effect observed for trust. Findings suggest that anthropomorphic cues, both visual and behavioral, may enhance perceptions of intelligence, relatability, and trust. This work contributes to the limited literature on anthropomorphism in industrial contexts and provides empirical evidence to guide the design of human-centered collaborative robotic systems. Full article

As human space missions become longer and more autonomous, robots are expected to assume broader responsibilities in inspection, maintenance, logistics, scientific support, and crew assistance. Among available robot forms, humanoid robotic astronauts are especially relevant because their anthropomorphic embodiment is compatible with human-centered habitats, tools, interfaces, and procedures. Their deployment in orbital and planetary environments, however, introduces challenges that differ from those of terrestrial humanoids, including floating-base dynamics, intermittent contact, whole-body coordination, constrained perception, and delayed supervision. This review contributes a mission-oriented and astronaut-centered synthesis of humanoid robotic astronauts, distinguishing itself from platform-by-platform or morphology-only surveys. It treats these systems as mission-compatible embodied agents whose feasibility depends on the coupling among mission context, morphology, contact behavior, perception, autonomy, and validation evidence. The primary goals are threefold: to classify representative platforms according to mission context, to synthesize the core technical foundations required for mission-compatible operation, and to identify cross-cutting deployment bottlenecks and benchmarking priorities for future development. Representative systems are organized into intravehicular assistance, extravehicular operations and on-orbit servicing, and surface exploration or transitional scenarios, showing how mission demands shape embodiment, mobility, manipulation, autonomy, and validation strategies. This review further summarizes recent progress in microgravity dynamics and contact mechanics, multimodal perception and scene understanding, whole-body motion planning and control, teleoperation and supervised autonomy, and evaluation and benchmarking methods. The analysis indicates that humanoid robotic astronauts are not simple extensions of terrestrial humanoids but astronaut-oriented embodied systems for mission-constrained environments. Three priorities are identified for future development: contact-rich whole-body intelligence under support transitions, delay-tolerant supervised autonomy with explicit authority handoff, and systematic benchmarking pipelines that connect simulation, ground analogs, short-duration microgravity tests, human-in-the-loop trials, and mission-context demonstrations. Full article

The peak–end rule proposes that retrospective evaluations depend on the emotional peak and the end of an experience rather than on its duration. Two short, controlled vocabulary-learning experiments tested whether optimizing these moments improves retrospective emotional valence. Study 1 (N = 32) manipulated task length (4 vs. 8 words). Retrospective emotional valence did not differ significantly between groups (p = 0.459, d = 0.27), a result consistent with duration neglect under this short task–episode manipulation but not a strong test of pure temporal duration neglect. Retrospective emotional valence correlated more strongly with the peak–end mean than with the mean of reconstructed page-level ratings (r = 0.761 vs. r = 0.314; Steiger’s Z = 3.03, p = 0.002). Study 2 (N = 56) used a 2 × 2 design to optimize the candidate peak-related completion page and the structurally defined end check-in page through color and anthropomorphic graphics. Both peak (ηp² = 0.18) and end (ηp² = 0.22) optimization enhanced retrospective emotional valence, with a significant non-additive interaction (ηp² = 0.09): the effect of optimizing one node was reduced when the other node had already been optimized. For learning accuracy, the main effect of peak optimization was significant (F(1, 52) = 4.44, p = 0.040), but only the combined peak-and-end optimization significantly outperformed the control condition (p = 0.041, d = 1.11); neither single-optimization condition significantly differed from the control condition after correction. The findings provide preliminary evidence for a peak–end-consistent evaluation pattern in brief, controlled vocabulary-learning tasks, identify a non-additive interaction in peak–end optimization, and offer guidance for designing key interactive moments within similarly short, task-based learning episodes. Full article

Social bots significantly threaten online public opinion through manipulation and misinformation, posing detection challenges due to high anthropomorphism and concealment. GNN methods show superior performance but face deployment hurdles on real-world platforms because of their reliance on multi-hop neighbor information during inference. Conversely, pure text-based methods lack collective behavior modeling and robustness against advanced bots. This paper proposes LKD, a social bot detection framework for graph-less deployment. The framework utilizes large language models to summarize historical tweets, compressing long-text information to construct multi-source inputs including metadata, profiles, and tweets. By employing a GNN as the teacher and a pre-trained LM as the student, LKD transfers structural knowledge to a text-based model via dual-objective knowledge distillation across prediction distributions and feature spaces. Experiments on Cresci-2015 and TwiBot-20 datasets show that the graph-less LKD-LM mode outperforms state-of-the-art methods in accuracy and F1-score. It maintains stable performance in label-scarce and sparse-graph scenarios, providing an efficient, robust solution for social media platforms with restricted interfaces or real-time requirements. Full article

This paper presents a fault-tolerant control (FTC) strategy for a six-degree-of-freedom (DoF) anthropomorphic manipulator operating under actuator faults and complex friction dynamics. The proposed framework integrates a backstepping control methodology with LuGre friction modeling and a feedforward neural network (FFNN) for friction estimation. Actuator faults are considered in the form of multiplicative efficiency losses and additive disturbances. An adaptive control law is developed to estimate and compensate for both friction and actuator faults in real time. The stability of the closed-loop system is guaranteed through Lyapunov theory. The simulation results validate the effectiveness and robustness of the proposed approach in ensuring precise trajectory tracking despite faults and friction uncertainties. Full article

This paper presents an immersive teleoperation framework for service robots that combines real-time 3D human pose estimation with a Virtual Reality (VR) interface to support intuitive, natural robot control. The operator is tracked using MediaPipe for 2D landmark detection and an Intel RealSense D455 RGB-D (Red-Green-Blue plus Depth) camera for depth acquisition, enabling 3D reconstruction of key joints. Joint angles are computed using efficient vector operations and mapped to the kinematic constraints of an anthropomorphic arm on the CHARMIE service robot. A VR-based telepresence interface provides stereoscopic video and head-motion-based view control to improve situational awareness during manipulation tasks. Experiments in real-world object grasping demonstrate reliable arm teleoperation and effective telepresence; however, vision-only estimation remains limited for axial rotations (e.g., elbow and wrist yaw), particularly under occlusions and unfavorable viewpoints. The proposed system provides a practical pathway toward low-cost, sensor-driven, immersive human–robot interaction for service robotics in dynamic environments. Full article

This study presents the design and evaluation of a dexterous prosthetic hand featuring five fingers, ten independently actuated joints, and four passively driven joints. The hand’s dexterity is enabled by a novel rigid–flexible coupled finger mechanism that incorporates a 1-active–1-passive joint configuration, which can enhance the dexterity of traditional rigid actuators while achieving a human-like workspace. Each finger is designed with a specific degree of rotational freedom to mimic natural opening and closing motions. This study also elaborates on the mapping of eight-channel electromyography to finger grasping force through improved TCN, as well as the control algorithm for grasping flexible objects. A functional prototype of the prosthetic hand was fabricated, and a series of experiments involving adaptive grasping and handheld manipulation tasks were conducted to validate the effectiveness of the proposed mechanical structure and control strategy. The results demonstrate that the hand can stably grasp flexible objects of various shapes and sizes. This work provides a practical solution for prosthetic hand design, offering promising potential for developing lightweight, dexterous, and highly anthropomorphic robotic hands suitable for real-world applications. Full article

An anthropomorphic robot is a mechanical device designed to perform human-like tasks, such as manipulating objects, and has been one of the significant contributions in robotics over the past 60 years. This paper presents an advanced system for controlling a robotic arm using user hand gestures and movements. It eliminates the need for traditional sensors or physical controls by implementing an intuitive approach based on MediaPipe and computer vision. The system recognizes the user’s hand movements. It translates them into commands that are sent to a microcontroller, which operates a robotic hand equipped with six servomotors: five for the fingers and one for the wrist, which stands out for its orthonormal design that avoids occlusion problems in turns of up to 180°, guaranteeing precise wrist control. Unlike conventional systems, this approach uses only a 2D camera to capture movements, simplifying design and reducing costs. The proposed system allows replicating the user’s activity with high precision, expanding the possibilities of human-robot interaction. Notably, the system has been able to replicate the user’s hand gestures with an accuracy of up to 95%. Full article

Twisted string actuators, which are an emerging artificial muscle, efficiently convert rotary motor motion into linear load movement, with advantages like high transmission ratio, compliance, simple structure, and long-distance power transmission. However, the limited range of transmission ratio adjustment remains a challenge. Thus, this paper introduces a novel twisted string actuator design that automatically and continuously adjusts its transmission ratio in response to external loads. Utilizing lightweight hyperelastic slender rods, the twisted string actuator with continuously variable transmission achieves a simple, compact, and cost-effective design. By manipulating the distance between two twisted strings through rod deformation, the transmission ratio continuously adapts to varying load conditions. Mathematical models of the twisted string actuator with continuously variable transmission are derived and experimentally validated, demonstrating a 2.1-fold transmission ratio variation from 0.1 kg to 1.5 kg loads. Application in an anthropomorphic robot finger showcases a 6.2-fold transmission ratio change between unloaded and loaded states. Our twisted string actuator with continuously variable transmission offers unparalleled advantages in weight, cost, simplicity, compliance, and continuous transmission ratio adjustability, making it highly suitable for robotic systems. Full article

Robot acceptance is rapidly increasing in many different industrial applications. The advancement of production systems and machines requires addressing the productivity complexity and flexibility of current manufacturing processes in quasi-real time. Nowadays, robot placement is still achieved via industrial practices based on the expertise of the workers and technicians, with the adoption of offline expensive software that demands time-consuming simulations, detailed time-and-motion mapping activities, and high competencies. Current challenges have been addressed mainly via path planning or robot-to-workpiece location optimization. Numerous solutions, from analytical to physical-based and data-driven formulation, have been discussed in the literature to solve these challenges. In this context, the machine learning approach has proven its superior performance. Nevertheless, the industrial environment is complex to model, generating extra training effort and making the learning procedure, in some cases, inefficient. The industrial problems concern workstation productivity; path-constrained minimal-time motions, considering the actuator’s torque limits; followed by robot vibration and the reduction in its accuracy and lifetime. This paper presents a procedure to find the robot base location for a prescribed task within the robot’s workspace, complying with multiple criteria. The proposed hybrid procedure includes analytical, physical-based, and data-driven modeling to solve the optimization problem. The contribution of the algorithm, for a given user-defined task, is the search for the best robot base location that enables the target points, maximizing the manipulability, avoiding singularities, and minimizing energy consumption. Firstly, the established method was verified using an anthropomorphic robot that considers different levels of a priori kinematics and system dynamics knowledge. The feasibility of the proposed method was evaluated through various simulations for small- and medium-sized robots. Then, a commercial offline program was compared, considering three scenarios and fourteen robots demonstrating an energy reduction in the 7.6–13.2% range. Moreover, the unknown joint dependency in real robot applications was investigated. From 11 robot positions for each active joint, a direct kinematic was appraised with an automatic DH scheme that generates the 3D workspace with an RMSE lower than 65.0 µm. Then, the inverse kinematic was computed using an ANN technique tuned with a genetic algorithm showing an RMSE in an S-shape task close to 702.0 µm. Finally, three experimental campaigns were performed with a set of tasks, repetitions, end-effector velocity, and payloads. The energy consumption reduction was observed in the 12.7–22.9% range. Consequently, the proposed procedure supports the reduction in workstation setup time and energy saving during industrial operations. Full article

This study explores manipulator control using reinforcement learning, specifically targeting anthropomorphic gripper-equipped robots, with the objective of enhancing the robots’ ability to safely exchange diverse objects with humans during human–robot interactions (HRIs). The study integrates an adaptive HRI hand for versatile grasping and incorporates image recognition for efficient object identification and precise coordinate estimation. A tailored reinforcement-learning environment enables the robot to dynamically adapt to diverse scenarios. The effectiveness of this approach is validated through simulations and real-world applications. The HRI hand’s adaptability ensures seamless interactions, while image recognition enhances cognitive capabilities. The reinforcement-learning framework enables the robot to learn and refine skills, demonstrated through successful navigation and manipulation in various scenarios. The transition from simulations to real-world applications affirms the practicality of the proposed system, showcasing its robustness and potential for integration into practical robotic platforms. This study contributes to advancing intelligent and adaptable robotic systems for safe and dynamic HRIs. Full article

This work describes the development of a tool for the teleoperation of robots. The tool is developed in a virtual environment using the Unity graphics engine. For the development of the application, a kinematic model and a dynamic model of a mobile manipulator are used. The mobile manipulator robot consists of an omnidirectional platform and an anthropomorphic robotic arm with 4 degrees of freedom (4DOF). The model is essential to emulate the movements of the robot and to facilitate the immersion in the virtual environment. In addition, the control algorithms are established and developed in MATLAB 2020 software, which improves the acquisition of knowledge to teleoperate robots and execute tasks of manipulation and transport of objects. This methodology offers a cheaper and safer alternative to real physical systems, as it reduces both the costs and risks associated with using a real robot for training. Full article

Dexterous manipulation of unknown objects performed by robots equipped with mechanical hands represents a critical challenge. The difficulties arise from the absence of a precise model of the manipulated objects, unpredictable environments, and limited sensing capabilities of the mechanical hands compared to human hands. This paper introduces a data-driven approach that provides a learning-based planner for dexterous manipulation employing an Adaptive Neuro-Fuzzy Inference System (ANFIS) fed by data obtained from an analytical manipulation planner. ANFIS captures the complex relationships between inputs and optimal manipulation parameters. Moreover, during a training phase, it is able to fine-tune itself on the basis of its experiences. The proposed planner enables a robot to interact with objects of various shapes, sizes, and material properties while providing an adaptive solution for increasing robotic dexterity. The planner is validated in a real-world environment, applying an Allegro anthropomorphic robotic hand. A link to a video of the experiment is provided in the paper. Full article