The identification of artistic style in art is important in conservation of cultural heritage, automated analysis, and interpretation. In Chinese art, which is ultimately very diverse and philosophically influenced, styles blend into each other, with nuances in symbols and time, making the computational classification task difficult. The idea of using AI and, to be more precise, deep learning in decoding intricate visual patterns seems to be prospective as far as the fine art is concerned. Low-level features are mostly used to classify styles in traditional style classification methods, which lack any contextual or interpretive richness; frameworks of CNN-based or deep matrix models fall into this category. In the case of Chinese paintings, these techniques are not sufficient because they were not able to cope with the ambiguity in style, regionalism and the fine strokes of brush and composition styles. Moreover, the majority of these models have been trained on the datasets of Western art and, therefore, they do not even strive to customize the classification logic in a culture-specific manner. The proposed study assumes a new self-regulatory model, which is grounded in a Vision Transformer (ViT) that will augment the accuracy and interpretability of Chinese artistic style detection. This paper presents a selfregulative layer that changes in prediction of classification Vision Transformers (ViT), but unlike normal ViTs, it actively refines uncertain predictions using confidence, attention, and consistency measures. This adaptation mechanism enhances the quality of predictions and transparency and the results of simulating human-like decision-making via an iterative feedback mechanism and reweighting of attention maps, thus, strengthening the interpretability of the ViT architecture. The ViT extracts intricate spatial and semantic features from image patches, while the self-regulatory layer checks the confidence in prediction, attention focus, and their consistency. Outputs deemed to be uncertain are dynamically re-routed or reprocessed. The decision refinement capability simulates that of an expert. Using the framework on a Chinese curated dataset based on WikiArt, the system achieved an accuracy of 98.55, the precision of 98.50, the recall of 98.53, and the F1-score of 98.50. These findings suggest the capability of the proposed style of modeling in fine art classification that other models may show in terms of robustness, adaptability, and cultural sensitivity compared to the traditional CNN and convolutional transformer models.

This paper explores the combination of leadership, innovation, and the new role of humanoid robots as leaders, with a focus on Generation Z’s perceptions. Through a structured survey of a convenience sample of 63 individuals in Poland, the study examines how various leadership styles are perceived in fostering innovation and whether humanoid robots can effectively assume leadership roles. The study investigates participants’ attitudes toward the potential advantages and drawbacks of humanoid robots in leadership. Findings reveal that while Generation Z is conscious of the importance of innovation, they are skeptical about humanoid robots as leaders, largely due to concerns about emotional intelligence, creativity, and ethical issues. The respondents favor democratic and transformational leadership styles for fostering innovation and express a strong preference for human oversight in AI-driven leadership roles. The study underlines the importance of ethical guidelines and accountability in the integration of AI into leadership.

In the health sector, robotics and Artificial Intelligence (AI) have greatly influenced the direction of patient care, diagnosis, and treatment outcomes, especially for elderly patients. With a view to ensuring real-time monitoring and quick medical response, the demand for sophisticated healthcare monitoring systems is rising with an aging population. Conventional medical monitoring methods often endure inefficiencies, including misclassifiability, vulnerability to noise, and processing slowdown, causing false diagnoses or lag in reacting to life-threatening disease. This model, a hybrid Visual Geometry Residual Network (VGRN), relies on VGG-16 strengths and the ability of ResNet-50 to counter such disadvantages and elevate classification accuracy in cases of illness. To enhance signal clarity and uniform feature representation, the system utilizes sophisticated data preprocessing tools like Fast Fourier Transform (FFT) for efficient feature extraction and Gaussian filtering to suppress noise. A robotic aid system is employed to offer emergency response, medication adherence monitoring, and cognitive support, allowing real-time monitoring and immediate action. Improved patient outcomes are the result of the greater predictive analytics and disease detection of an AI-driven solution. With its remarkable accuracy of 98.52% and precision rate of 98.24%, the test result confirms the efficiency of the proposed system and far exceeds traditional approaches to health monitoring. The technology, which integrates intelligent robotic care and AI diagnostics, not only enhances the effectiveness of treatment but also reduces caregiver workloads, making elder care more convenient and efficient. By using sophisticated technology, the proposed model has an important role to play in enabling proactive management of healthcare, minimizing emergency situations, and ultimately enhancing the elderly’s quality of life.

In motion planning of biped robots, the place where the swing foot lands on the ground is crucial. Stability, energy consumption, smoothness of movement as well as naturalness of motion can be depending on the selected foot placement. In this paper, a novel method based on the A* algorithm and kinematic manipulability for foot placement planning is presented. In this method, by calculating the magnitude and direction of the maximum manipulability for the candidate foot placements, one is selected that leads to the most dexterous and agile walking while maintaining stability. The advantage of this approach is avoiding singularities and being ready to create a higher potential for foot velocities. Therefore, the possibility of performing maneuvers to maintain walking stability would be enhanced. The mentioned algorithm has been successfully implemented on a simulated three-dimensional biped robot.

A humanoid robot is a particular form of embodied agent. The form that an agent takes has a major impact on how that agent interacts with its environment and how it develops an understanding of that environment through its interactions. In this paper, we explore the importance of humanoid embodiment and we argue that humanoids occupy a special niche in the spectrum of robot forms. In doing so, we highlight the implications for the way a humanoid robot can interact with its environment, including humans, for the manner in which humans interact with humanoid robots, and for a humanoid robot’s capacity to develop cognitive abilities. We also consider the degree to which humanoid robots should approximate humans, addressing robot morphology, appearance, and movement. We emphasize the dual role of humanoid robots as engineering artifacts that can provide services for humans, and as platforms for scientific enquiry into the nature of human cognition. We conclude by highlighting some key research challenges for the discipline of humanoid robotics.

This paper presents efficient geometric algorithms to analytically evaluate the first- and second-order information of humanoid dynamics within a trajectory optimization scheme. In particular, the exact Hessians of the robot’s center of mass and the centroidal momentum are provided to the optimization solver for synthesizing dynamically feasible humanoid motions. The transcription method applies direct collocation to parametrize the humanoid’s configuration space with B-Spline curves. The proposed algorithms avoid the computation of redundant information by exploiting the commutativity of the Kronecker product and the symmetry in second-order derivatives. We validate the effectiveness of the optimization strategy with the humanoid robot models Nao and Atlas. (The C++ code can be found in github.com/AlvaroPaz/BSplinesTrajOpt.)

This paper presents a new control approach for a humanoid biped robot to perform highly dynamic locomotion and keep balance under large external disturbances. The proposed control approach mainly contains two parts: a simplified model-based task planner and an online whole-body controller. The periodic stability is achieved with a foot placement policy presented in this paper. The landing position is predicted and adjusted at every time step to adapt to uncertain disturbances. Then the desired task trajectories are tracked with a quadratic programming (QP) based task space whole-body controller, while satisfying some specific physical constraints. The whole-body controller outputs the optimized joint accelerations, which are then used to produce desired joint torques for the robot’s actuators. The positions of the center of mass (CoM) along sagittal and lateral axes are not controlled in the whole-body controller. Meanwhile, the torques at the ankles are constrained to be small and the robot follows a passive-like behavior, which is energetically efficient and helps the feet of the robot to fit better in the environment. Simulations on several highly dynamic gaits have been conducted, two examples among which are presented in this paper. The first demonstrates a walking gait with an increasing sagittal speed and its capability to resist large external disturbances. In the second example, the robot is able to run stably on uneven terrain without knowing any information about it.

To address the issue of error-prone and unstable trajectory tracking and dynamic obstacle avoidance of mobile robots in locally observable nonlinear dynamic settings, a deep reinforcement learning (RL)-based visual perception, and decision-making system is proposed. The technique creates a closed loop between the system’s environmental perception and decision-making capabilities by combining the perceptual capabilities of convolutional neural networks with the decision-making capabilities of RL in a generic form. It achieves direct output control from the visual perception input of the environment to the action through end-to-end learning. The simulation results show that this approach is capable of meeting the demands of multi-task intelligent perception and decision making. It also more effectively addresses issues with traditional algorithms, including their tendency to fall into local optimums, oscillate in groups of similar obstacles without recognizing the path, oscillate in tight spaces and inaccessible targets close to obstacles and significantly enhance real-time and adaptability of robot trajectory tracking and dynamic obstacle avoidance.

The gait cycle of 25-degree of freedom (DOF) humanoid robot, namely NAO robot, consists of single support phase (SSP) and double support phase (DSP). Both dynamic and stability analyses are carried out for this robot to determine its power consumption and dynamic stability margin, respectively. Constrained single-objective optimization problems are formulated for the SSP and DSP separately and solved using particle swarm optimization (PSO) and genetic algorithms (GA). A performance index, other than the fitness function, consisting of constraint values and maximum swing height, is also considered to compare PSO and GA-obtained optimal solutions. PSO is able to find the trajectories that offer higher swing height for nearly similar power consumption during SSP. A performance assessment of each algorithm based on the best fitness values in each generation across several runs is also carried out. These values are compared using the Wilcoxon rank-sum test, and PSO is found to be statistically better than GA. The optimal solutions from the simulations are tested using the Webots simulator to validate their efficacy on stability. Moreover, an investigation of the influence of gait parameters on power consumption during SSP and DSP reveals that the humanoid robot with a higher hip height, lower swing height, and slow pace consumes less power. The methodology developed in this is generic and can be easily extended to other robots.

Gait pattern performance, for its crucial significance in humanoid robot stabilization, has attracted the attention of researchers worldwide. Although simplified models highlight major features, bipedal walking has bewildered the researchers. Therefore, for a precise understanding of the bipedal model, a state-of-the-art, simplified model has been proposed in this paper which comprises a 3D-multilinked dual spring-loaded inverted pendulum (3D-MDSLIP) while acknowledging the vertical fluctuations of the center of mass (CoM). In addition, the model considers upper body movement and its effects on the stabilization of the humanoid robot. The mathematical modeling of a humanoid walking over the obstacle and slope is demonstrated to precisely understand the problem. The tuning process of the parameters and postures in a humanoid robot is complex and time-consuming. For proper walking of a robot over uneven terrains and slopes, tuning of the PID controller is achieved using converged teaching-learning based optimization (CTLBO) technique for a central pattern generator (CPG) gait, as introduced in the paper. The optimal gait angles are applied to the experimental and simulated NAO to successfully navigate the provided terrain. Thus, the experimental and simulation results jointly show that the proposed CPG-CTLBO gait learning technique is feasible for finding an optimal gait pattern for the humanoid robot within a deviation of 5%. The energy efficiency of the proposed controller is compared with the default controller of NAO based on the average electronic current in sagittal and lateral movement. Further, it is examined for the energy consumption for several slopes, and the results obtained are acceptable, showing the controller is efficient. Additionally, it has been compared with an existing technique for walking a humanoid robot on uneven terrains. The graph obtained using the proposed technique demonstrates the superiority of the proposed technique.