Eduardo Benítez Sandoval

Jasmin Bernotat, Doreen Jirak, Eduardo Benitez Sandoval et al.

We present a research outline that aims at investigating group dynamics and peer pressure in the context of industrial robots. Our research plan was motivated by the fact that industrial robots became already an integral part of human-robot co-working. However, industrial robots have been sparsely integrated into research on robot credibility, group dynamics, and potential users' tendency to follow a robot's indication. Therefore, we aim to transfer the classic Asch experiment (see \cite{Asch_51}) into HRI with industrial robots. More precisely, we will test to what extent participants follow a robot's response when confronted with a group (vs. individual) industrial robot arms (vs. human) peers who give a false response. We are interested in highlighting the effects of group size, perceived robot credibility, psychological stress, and peer pressure in the context of industrial robots. With the results of this research, we hope to highlight group dynamics that might underlie HRI in industrial settings in which numerous robots already work closely together with humans in shared environments.

Marsha Mariya Kappan, Eduardo Benitez Sandoval, Erik Meijering et al.

Pose estimation is a critical task in computer vision with a wide range of applications from activity monitoring to human-robot interaction. However,most of the existing methods are computationally expensive or have complex architecture. Here we propose a lightweight attention based pose estimation network that utilizes depthwise separable convolution and Convolutional Block Attention Module on an hourglass backbone. The network significantly reduces the computational complexity (floating point operations) and the model size (number of parameters) containing only about 10% of parameters of original eight stack Hourglass network. Experiments were conducted on COCO and MPII datasets using a two stack hourglass backbone. The results showed that our model performs well in comparison to six other lightweight pose estimation models with an average precision of 72.07. The model achieves this performance with only 2.3M parameters and 3.7G FLOPs.

William Z. Ye, Eduardo B. Sandoval, Pamela Carreno-Medrano et al.

Robotics has been a popular field of research in the past few decades, with much success in industrial applications such as manufacturing and logistics. This success is led by clearly defined use cases and controlled operating environments. However, robotics has yet to make a large impact in domestic settings. This is due in part to the difficulty and complexity of designing mass-manufactured robots that can succeed in the variety of homes and environments that humans live in and that can operate safely in close proximity to humans. This paper explores the use of contextual affordances to enable safe exploration and learning in robotic scenarios targeted in the home. In particular, we propose a simple state representation that allows us to extend contextual affordances to larger state spaces and showcase how affordances can improve the success and convergence rate of a reinforcement learning algorithm in simulation. Our results suggest that after further iterations, it is possible to consider the implementation of this approach in a real robot manipulator. Furthermore, in the long term, this work could be the foundation for future explorations of human-robot interactions in complex domestic environments. This could be possible once state-of-the-art robot manipulators achieve the required level of dexterity for the described affordances in this paper.

Zihan Lin, Francisco Cruz, Eduardo Benitez Sandoval

Emotion recognition plays a crucial role in various domains of human-robot interaction. In long-term interactions with humans, robots need to respond continuously and accurately, however, the mainstream emotion recognition methods mostly focus on short-term emotion recognition, disregarding the context in which emotions are perceived. Humans consider that contextual information and different contexts can lead to completely different emotional expressions. In this paper, we introduce self context-aware model (SCAM) that employs a two-dimensional emotion coordinate system for anchoring and re-labeling distinct emotions. Simultaneously, it incorporates its distinctive information retention structure and contextual loss. This approach has yielded significant improvements across audio, video, and multimodal. In the auditory modality, there has been a notable enhancement in accuracy, rising from 63.10% to 72.46%. Similarly, the visual modality has demonstrated improved accuracy, increasing from 77.03% to 80.82%. In the multimodal, accuracy has experienced an elevation from 77.48% to 78.93%. In the future, we will validate the reliability and usability of SCAM on robots through psychology experiments.

Eduardo Benitez Sandoval, Ricardo Sosa, Massimiliano Cappuccio et al.

Creativity in social robots requires further attention in the interdisciplinary field of Human-Robot Interaction (HRI). This paper investigates the hypothesised connection between the perceived creative agency and the animacy of social robots. The goal of this work is to assess the relevance of robot movements in the attribution of creativity to robots. The results of this work inform the design of future Human-Robot Creative Interactions (HRCI). The study uses a storytelling game based on visual imagery inspired by the game 'Story Cubes' to explore the perceived creative agency of social robots. This game is used to tell a classic story for children with an alternative ending. A 2x2 experiment was designed to compare two conditions: the robot telling the original version of the story and the robot plot-twisting the end of the story. A Robotis Mini humanoid robot was used for the experiment. As a novel contribution, we propose an adaptation of the Short Scale Creative Self scale (SSCS) to measure perceived creative agency in robots. We also use the Godspeed scale to explore different attributes of social robots in this setting. We did not obtain significant main effects of the robot movements or the story in the participants' scores. However, we identified significant main effects of the robot movements in features of animacy, likeability, and perceived safety. This initial work encourages further studies experimenting with different robot embodiment and movements to evaluate the perceived creative agency in robots and inform the design of future robots that participate in creative interactions.

Eduardo Benitez Sandoval, Diego Vazquez Rojas, Clarissa A. Parada Cereceres et al.

This paper describes a case study and the insights gained from prototyping an Intelligent Virtual Agent (IVA) for English vocabulary building for Spanish-speaking preschool children. After an initial exploration to evaluate the feasibility of developing an IVA, we followed a Human-Centered Design (HCD) approach to create a prototype. We report on the multidisciplinary process used that incorporated two well-known educative concepts: gamification and story-telling as the main components for engagement. Our results suggest that a multidisciplinary approach to developing an educational IVA is effective. We report on the relevant aspects of the ideation and design processes that informed the vision and mission of the project.

Shreepriya Gonzalez Jimenez, Danilo Gallo, Ricardo Sosa et al.

The design and development of robots involve the essential step of selecting and testing robotic interfaces. This interface selection requires careful consideration as the robot's physical embodiment influences and adds to the traditional interfaces' complexities. Our paper presents a decision support design framework for the a priori selection of robotic interface that was inductively formulated from our case study of designing a robot to collaborate with employees with cognitive disabilities. Our framework outlines the interface requirements according to User, Robot, Tasks and Environment and facilitates a structured comparison of interfaces against those requirements. The framework is assessed for its potential applicability and usefulness through a qualitative study with HRI experts. The framework is appreciated as a systematic tool that enables documentation and discussion, and identified issues inform the frameworks' iteration. The themes of ownership of this process in interdisciplinary teams and its role in iteratively designing interfaces are discussed.