Beyond environmental conservation, the broader sustainable development of social robotics requires the efforts of diversified practices, in which the widest possible scope of cultures and perspectives contribute to HRI discourses. The field of Cultural Robotics has developed beyond recognising the deep cultural impact of social robotics, and practitioners must now look to platform the sharing and integration of culturally developed sustainable methodologies. Embedded within different cultures are a rich variety of collaborative practices of knowledge sharing, community leasing of resources, methods for reusing, repairing and customising technology, and contemporary recycling methods. These circular economy principles have an integral application to social robotics, in managing the long-term environmental impact of robotics, as well as ensuring this social technology is developed in the most inclusive way possible. In this workshop, participants engage with topics such as eco-friendly material selection, ethics for human-robot coevolution, lean and modular design methodologies, and strategies for ensuring robotics applications are adaptable across different cultural contexts. The workshop features expert presentations, interactive discussions, and hands-on activities that reflect emerging practices in sustainable and culturally informed robotics. Contributions are invited from diverse cultural perspectives to explore how social robotics development can effectively integrate circular economy principles to support environmentally responsible and socially inclusive innovation. Selected contributors will present during the workshop panel sessions, and all accepted papers will be compiled into a comprehensive publication, serving as a valuable resource for practitioners and researchers dedicated to advancing sustainability and cultural integration in robotics development.

This work presents an algorithm based on the Nondominated Sorting Genetic Algorithm II (NSGA-II) to solve multi-objective offline Multi-Robot Coverage Path Planning (MCPP) problems. The proposed algorithm embeds a donation-mutation operator and a multiple-parent crossover that generates solutions which maintain the longest path while minimizing the average path length. The algorithm also uses a library of elitism-selected high-fitness robot paths, and tournament-selected high min-max fitness paths, to construct high multi-objective fitness offspring. We evaluate the performance of our proposed algorithm against the state-of-the-art NSGA-II extended with an improved Heuristic Genetic Algorithm Crossover, and we demonstrate that for different instances of the MCPP problem, the Pareto-fronts of our proposed algorithm are not dominated by any of the points of the fronts generated by the state-of-the-art NSGA-II. A comparison has also been performed in a virtual environment simulating five drones inspecting three wind turbines. Results show that our approach exhibits a higher convergence rate for higher values of the ratio between the number of points to visit and the number of drones.

Motivated by modeling a general behavioral function of a target system, a data-driven multivariable support vector regression (SVR) is developed. The multivariable SVR is sought to estimate a generalized relationship among multiple input variables which would be collected locally from a distributed multi-sensor network. Instead of an immediate estimation with a installed single sensor, the participation of distributed multi sensors gains the estimation of the system states more reliable and more generalized. The proposed SVR modeling method gives rise of the possibility to support multivariable input spectrum, and the flexibility to combine the input variables with respective decay weights. The paper herewith elaborates the development with experimental evidences.

As the emerging development of IoT circumstance, on-line detections or observations of a system states become easier by facilitating its corresponding multi-sensory responses, and thus the description of a system behavior becomes clearer. Abundant on-line multi-channel information from the embedded sensors would be advantageous to the understanding of the system. Though having the information, it is still uneasy to thoroughly assess the integrity behavior of the system without a eligible system identification method. In the study, through a recurrent function approximation by an integrated multi-regression of support vector regression (SVR), the identification has been developed and represented as a family of characteristic functions. With the set of characteristic functions, a design of IoT based live interaction of adaptive control or human-machine system could hereafter followed up. The study constructed primarily the SVR based framework of the recurrent multi-sensor system identification. There are two major contributions of the study: First, an IoT aspect for the discovery of the multiple regression extended from an underlying SVR, second, a technical overcoming in the realization of the recurrent framework of SVR.

A novel robot with the purpose of translation is presented in this paper. The proposed system consists of a mobile robot which carries a smart phone that language translation APP, including audio, text and audio, is running on it. It also equipped with extra touch and voice sensors for input, micro controller for process and speaker and wheels for output. The proposed system is equipped with state flow mechanism, which is managed by an artificial endocrine system that employs the PID controller in order to navigate between interacting people. This robot provides an embodied system which could function as a robotic translator that navigates between interacting people and facilitates the communication.

A new approach in telepresence robots via automatic behavior generation is proposed in this paper. The robot behavior is generated using the Smartphone high level sensing, and personality based mood transition. The current telepresence systems require the simultaneous presence of both communication parties. Therefore, due to the time and context differences in distant communication, the opportunity of connectedness is reduced. This is especially important for more intimate telecommunications, that ongoing connectedness is more required. To solve these problems, we developed an automatic behavior generation system, that produces behaviors on behalf of the remote person. In order to be able to infer the state of the remote person more frequently and dynamically, Smartphone sensors are used for automatic high level sensing. Furthermore, to produce more believable affective expressions by the robot, the expressions correspond to the remote user's personality stereotype. Fuzzy Kohonen Clustering Network (FKCN), is utilized to linearly fuse the inferred mood states and regenerate them on the agent. To evaluate the results 120 samples of the user's Smartphone usage and the mood in different times of the day were logged. The state estimated by the model were compared against the users self report data. The results showed that there was no significant difference between the user's self-perceived feelings, and the feelings recognized by the model.

Recent advances in human-robot interaction have enhanced the ability of robots to act as a mediator in remote communication. However, embodied expressive communication across distance is not supported by the current technology. Physical body is a powerful conduit for affective communication, and thus mediating properties of the physical body through an agent has been proposed to address this deficiency. In this paper, we offer heuristic justification on vision of embodied interfaces by adopting perspectives from social psychology in the context of mediated interpersonal communication. Based on our studies, we created a pair of robots named “Mini-Surrogate” as a personalized telecommunication medium which recreates and emulates the physical presence of a specific person. It applies the concepts of enclothed and embodied cognition to convey the illusion of a particular person's presence. Through minimal prototyping in terms of surrogate behaviors, we explored desired nonverbal cues by remotely located couples to satisfy their communication needs while being apart. Our exploratory study pointed to the acceptance of prototyped medium and showed that personalization could enhance communication affectivity in terms of providing awareness and reminding of the specific person. We realized the desired nonverbal cues that helped remote couples to perceive the interface as their remote partner. The findings contribute towards user experience, communication and robotic researchers to enhance the quality of telecommunication for remote couples.

In this paper, we present Kissenger (Kiss Messenger), an interactive device that provides a physical interface for transmitting a kiss between two remotely connected people. Each device is paired to another and can sense and transmit the amount of force that a user applies to a pair of lips which is recreated on the other device using motors. Kissenger was designed to augment already existing remote communication technologies such as video chat. The goal of this work is to promote intimacy between humans in long distance relationships. After presenting the background and motivation for the need of such a device, we describe the design process that consisted of three iteration stages, each with its own focus and evaluation. We then present a preliminary user study performed with seven couples that compare Kissenger to current video chat technology.

In order to develop a close relationship between humans and robots, we proposed a multi-modal sentimental model which considers both long and short term affective parameters of interaction. Our model is inspired from scientific studies of love in humans and aims to generate a bi-directional love between humans and robots. We refer to this sentimental connection as “Lovotics”. We have formulated probabilistic mathematical models for identified factors of love, and aim to provide a clear, distinct and discrete interpretation of the intimacy between humans and robots. Such mathematical models are assembled by a Bayesian Network depicting the relationship between intimacy and the causal factors for love. Furthermore, a novel affective state transition system is proposed which takes into account not only the current state caused by interactions, but also the effects of the previous states and internal factors of the robot. Hence, the robot is capable of acting consistently and naturally. The behavior of the robot is controlled by the above two modules via an Artificial Neural Network to develop a realistic affective communication between a human and a robot.

Lovotics is a novel field of research that strives to achieve a close relationship between humans and robots by utilizing fundamental concepts from psychology, biology, neuroscience, and robotics to develop a model that effectively imitates human affection process to create an emotionally engaging robotic system with high level of intimacy. We have designed and developed a robotics system that is an active participant in a bi-directional non verbal communication process with humans, and uses audio and touch channels along with internal state parameters in order to establish long standing bonds with individuals. The novel Affective Intelligence system of Lovotics includes an Artificial Endocrine System and an Intimacy Module. These two modules work in tandem to develop intimate interactions between the robot and humans.

In this paper, we present an active vision system for human-robot interaction purposes that includes robust face detection, tracking, recognition and facial expression analysis. The system will search for human faces in view, zoom on the face of interest based on the face recognition database, track it and finally analyze the emotion parameters on the face. After detection using Haar-cascade classifiers, the variable parameters of the camera are changed adaptively to track the face of the subject by employing the Camshift algorithm, and to extract the facial features which are used for face recognition and facial expression analysis. Embedded Hidden Markov Model is used for face recognition and nonlinear facial mass-spring model is employed to describe the facial muscle’s tension. The motion signatures are then classified using Multi-layer Perceptrons for facial expression analysis. This system can be used as a comprehensive and robust vision package for a robot to interact with human beings.

Omni directional mobile robots have been popularly employed in several applications especially in soccer player robots in Robocup competitions. However, control and self-localization of omni directional mobile robots are important issues and different teams in the Robocup competitions have used different techniques to tackle it. Since it is very complicated to calculate the omni directional system transfer function as in classic control, a simplified model of the system would be helpful for estimating the PID coefficients of the robots' position and orientation control. The vision-based self-localization combined with the odometry system enables us to have a robust self-localization method. The findings have been tested in the Robocup competition field using three Persia middle size omni directional robots.