Robot Avatar Research Articles

Evaluation of an avatar robot with interactive atmosphere in online communication

Evaluation of an avatar robot with interactive atmosphere in online communication

Bimanual telemanipulation with force and haptic feedback through an anthropomorphic avatar system

Robotic teleoperation is a key technology for a wide variety of applications. It allows sending robots instead of humans in remote, possibly dangerous locations while still using the human brain with its enormous knowledge and creativity, especially for solving unexpected problems. A main challenge in teleoperation consists of providing enough feedback to the human operator for situation awareness and thus create full immersion, as well as offering the operator suitable control interfaces to achieve efficient and robust task fulfillment. We present a bimanual telemanipulation system consisting of an anthropomorphic avatar robot and an operator station providing force and haptic feedback to the human operator. The avatar arms are controlled in Cartesian space with a direct mapping of the operator movements. The measured forces and torques on the avatar side are haptically displayed to the operator. We developed a predictive avatar model for limit avoidance which runs on the operator side, ensuring low latency. The system was successfully evaluated during the ANA Avatar XPRIZE competition semifinals. In addition, we performed in lab experiments and carried out a small user study with mostly untrained operators.

Accessible Parallel Embodiment of Multiple Robotic Avatars in a Cafe

Novel technological systems enabling people to control multiple robotic avatars simultaneously could support more accessible ways to multitask. An implementation of a parallel avatar system in a cafe shows that, leveraging their existing skills, remote workers with disabilities were able to utilize the system to control up to four robots, each with a different function, to assist customers in a cafe. The parallel avatar systems expand the agency and the capabilities of disabled workers, while also delivering better experiences to customers.

Guidance in storytelling tables supports emotional development in kindergartners

Promoting the social-emotional development of kindergartners is of special relevance as will lay the foundations for emotion regulation in later childhood and adulthood stages. Considering that tangible storytelling tables are already used for language and literacy skills in kindergarten, we addressed the problem of designing a storytelling intervention aimed at social-emotional development suitable in such a context by using an emotional laden story as content and embedding a guidance method that can be implemented with either a human or robot guide to enhance the learning setting. The study considered two guided storytelling activities (one traditional guided by the teacher, and one in which guidance was provided by a robot) and a control condition without additional guidance. The three conditions were compared in terms of kindergartners’ enactment process, an emotion recognition test and a story recall test. The results show that the guidance method properly supported emotion naming, children involvement and goal completion during the storytelling activity whereas the intervention supported the learning gain on emotion recognition. The study revealed that both robot and human guidance did not differ significantly in the performance tests but did outperform the control. In view of the results, this research is helpful for researchers and teachers to create in an informed way a range of environments in the kindergarten class based on storytelling tables, either with or without guidance, and with or without robot support. Future work may further investigate how specific interaction issues concerning robot embodiment (e.g., voice and behavioral cues to direct children’s attention) might enhance or not the children’s performance.

Designing for culturally responsive social robots: An application of a participatory framework.

Integrating cultural responsiveness into the educational setting is essential to the success of multilingual students. As social robots present the potential to support multilingual children, it is imperative that the design of social robot embodiments and interactions are culturally responsive. This paper summarizes the current literature on educational robots in culturally diverse settings. We argue the use of the Culturally Localized User Experience (CLUE) Framework is essential to ensure cultural responsiveness in HRI design. We present three case studies illustrating the CLUE framework as a social robot design approach. The results of these studies suggest co-design provides multicultural learners an accessible, nonverbal context through which to provide design requirements and preferences. Furthermore, we demonstrate the importance of key stakeholders (students, parents, and teachers) as essential to ensure a culturally responsive robot. Finally, we reflect on our own work with culturally and linguistically diverse learners and propose three guiding principles for successfully engaging diverse learners as valuable cultural informants to ensure the future success of educational robots.

Sensorimotor Control Sharing With Vibrotactile Feedback for Body Integration Through Avatar Robot

An avatar robot can be operated by multiple users, augmenting the operation of a single user. In this study, we assembled a collaborative operation system for a 7-DoF robotic arm with a gripper controlled by two users. The users share the role of controlling the robot with one user controlling the arm and the other controlling the gripper. The actions of the two users must be seamlessly coordinated to ensure smooth and precise operations. Therefore, this study aimed to investigate vibrotactile feedback to promote recognition of the actions of a partner. A pick-and-place task was considered as a basic operation. First, an intensity adjustment was developed for vibrotactile feedback to ensure linear sensitivity and its usefulness was verified. An estimation test for the reaching motion showed that the target position was estimated accurately based on velocity feedback, indicating that the participants imaged the arm motion. Subsequently, collaboration tests with and without vibrotactile velocity feedback revealed a potential effect of vibrotactile feedback in terms of reducing the time required for completing the target task. Our experimental results indicate that vibrotactile feedback for partner's motion can be applied to achieve smooth collaborative operations using an avatar robot.

The use of AV1 robot avatars in schools – A tool for inclusion?

In recent years, there has been promising research regarding the use of the AV1 robot avatar, a telepresence device that allows children to access classroom learning remotely. Given the AV1’s promise in supporting children with long term illnesses to access their education, there is a notable gap within published, empirical research regarding the use of AV1s within the UK. In light of this, the proposed project aimed to explore the current use and impact of the AV1s, from the perspectives of UK strategic leads, to inform possible UK research for using AV1s within schools. Semi-structured interviews were held with two strategic leads. Discussions identified four prominent themes regarding the use and impact of the AV1s: Uses, Outcomes, Facilitators and Barriers. These four themes and limitations of the study are discussed, alongside implications for future research.

A Cross-Cultural Comparison on Implicit and Explicit Attitudes Towards Artificial Agents

Historically, there has been a great deal of confusion in the literature regarding cross-cultural differences in attitudes towards artificial agents and preferences for their physical appearance. Previous studies have almost exclusively assessed attitudes using self-report measures (i.e., questionnaires). In the present study, we sought to expand our knowledge on the influence of cultural background on explicit and implicit attitudes towards robots and avatars. Using the Negative Attitudes Towards Robots Scale and the Implicit Association Test in a Japanese and Dutch sample, we investigated the effect of culture and robots’ body types on explicit and implicit attitudes across two experiments (total n = 669). Partly overlapping with our hypothesis, we found that Japanese individuals had a more positive explicit attitude towards robots compared to Dutch individuals, but no evidence of such a difference was found at the implicit level. As predicted, the implicit preference towards humans was moderate in both cultural groups, but in contrast to what we expected, neither culture nor robot embodiment influenced this preference. These results suggest that only at the explicit but not implicit level, cultural differences appear in attitudes towards robots.

How Does Virtual Reality Compare? The Effects of Avatar Appearance and Medium on Self-Disclosure

Digital communication methods such as social media, texting, video conferencing account for a significant amount of social interaction traffic; however, in the coming decade, virtual reality (VR) is likely to appropriate much of this space from traditional 2D computer-mediated applications. The current series of studies first compared how different digital communication mediums (i.e., voice only, video chat, and VR) affect self-disclosure and then, looking specifically at VR, evaluated how avatar appearance affects self-disclosure. Overall, study one indicated that VR users are more likely to reveal highly personal facts, attitudes, opinions, and emotions than video chat or voice only communication users. Looking only at VR communication, study two demonstrated that individuals with a human avatar are more likely to reveal emotional experiences and highly personal facts, attitudes, and emotions about themselves and their experiences compared to individuals using a robot avatar.

Designing and Evaluating Presentation Avatar for Promoting Self-Review

Self-review is essential to improving presentation, particularly for novice/unskilled researchers. In general, they could record a video of their presentation, and then check it out for self-review. However, they would be quite uncomfortable due to their appearance and voice in the video. They also struggle with in-depth self-review. To address these issues, we designed a presentation avatar that reproduces presentation made by researchers. The presentation avatar intends to increase self-awareness through self-reviewing. We also designed a checklist to aid in a detailed self-review, which includes points to be reviewed. This paper also demonstrates presentation avatar systems that use a virtual character and a robot, to allow novice/unskilled researchers as learners to self-review their own presentation using the checklist. The results of case studies with the systems indicate that the presentation avatar systems have the potential to promote self-review. In particular, we found that robot avatar promoted engagement in self-reviewing presentation.

Human-robot creative interactions: Exploring creativity in artificial agents using a storytelling game.

Creativity in social robots requires further attention in the interdisciplinary field of human–robot interaction (HRI). This study investigates the hypothesized 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 2 × 2 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, and we adapted the Short Scale of Creative Self (SSCS) to measure perceived creative agency in robots. We also used 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.

Examining Individual and Team Phenomena in the Context of Robots and AI Agents

As AI-led robotics is a newly evolving technology that is steadily impacting organizational practice, it is crucial and timely for management research to study the consequent effects on employees. Hence, to bring to light current research being conducted on (embodied and unembodied) robotics from a behavioral perspective, this Symposium brings together five papers, each of which discusses a management phenomenon (collaboration, perception, conflict, performance, leadership) at the individual- or group-level. The purpose of this Symposium is to provide OB and management scholars several specific examples of robotics research and to offer a platform for a broader conversation about the future of behavioral research in the AI era. The Symposium will also allow the presenters and organizers to learn from each other about the array of opportunities available in behavioral management for robotics research - from extending the boundaries of micro-level theories to contemporizing conventional research questions and discussing practical and policy implications. Lastly, as several of the Symposium presenters/lead authors are doctoral and post-doctoral candidates, student attendees interested in robotics research will have the opportunity to connect and engage in dissertation-related conversations. Robot Avatar in Virtual Teams: A Potential Solution to Conflict? Presenter: FUHE JIN; Binghamton U.-State U. of New York Presenter: Chun-Han Chao; Binghamton U.-State U. of New York Presenter: Biying Yang; Binghamton U.-State U. of New York Presenter: Minjong Jun; Binghamton U.-State U. of New York Worker Attitudes Towards Robotic and AI Agents Presenter: Daria Morozova; HEC Paris Presenter: Mathis Schulte; HEC Paris Navigating (Dystopian) Imaginaries of the Future of Work: An Ethnography of Robotics Creatives Presenter: Philip Rogiers; KU Leuven Presenter: Nicky Dries; KU Leuven Presenter: Joost Luyckx; KU Leuven Super Mario Meets AI Agent Presenter: Fabrizio Dell'Acqua; Columbia Business School Presenter: Bruce Kogut; Columbia Business School Presenter: Patryk Perkowski; Columbia Business School Human-Robot Collaboration in Organizations: A Multilevel and Complementary Perspective Presenter: Chou-Yu Tsai; Binghamton U.-State U. of New York Presenter: Jason Marshall; Creighton U. Presenter: Anwesha Choudhury; Binghamton U.-State U. of New York Presenter: Andra Serban; Virginia Commonwealth U. Presenter: Tsung-Yu Hou; Cornell U. Presenter: Malte Friedrich Jung; Stanford U. Presenter: Shelley D Dionne; Binghamton U.-State U. of New York Presenter: Francis J. Yammarino; Binghamton U.-State U. of New York

Multiple Groups of Agents for Increased Movement Interference and Synchronization.

We examined the influence of groups of agents and the type of avatar on movement interference. In addition, we studied the synchronization of the subject with the agent. For that, we conducted experiments utilizing human subjects to examine the influence of one, two, or three agents, as well as human or robot avatars, and finally, the agent moving biologically or linearly. We found the main effect on movement interference was the number of agents; namely, three agents had significantly more influence on movement interference than one agent. These results suggest that the number of agents is more influential on movement interference than other avatar characteristics. For the synchronization, the main effect of the type of the agent was revealed, showing that the human agent kept more synchronization compared to the robotic agent. In this experiment, we introduced an additional paradigm on the interference which we called synchronization, discovering that a group of agents is able to influence this behavioral level as well.

PARTI-A Haptic Virtual Reality Control Station for Model-Mediated Robotic Applications

In this paper, we introduce a tele-robotic station called “PARTI” that leverages state-of-the-art robotics and virtual reality technology to enable immersive haptic interaction with simulated and remote environments through robotic avatars. Our hardware-in-the-loop framework integrates accurate multibody system dynamics and frictional contacts with digital twins of our robots in a virtual environment with real-time computational capabilities. This model mediated hardware-in-the-loop approach to robotic control allows a teleoperator to use the PARTI system to teach, evaluate, and control various robotic applications. In the current contribution, we focus on the general system description, integrated simulation and control framework, and a series of experiments highlighting the advantages of our approach.

Robot Learning of Mobile Manipulation With Reachability Behavior Priors

Mobile Manipulation (MM) systems are ideal candidates for taking up the role of a personal assistant in unstructured real-world environments. Among other challenges, MM requires effective coordination of the robot's embodiments for executing tasks that require both mobility and manipulation. Reinforcement Learning (RL) holds the promise of endowing robots with adaptive behaviors, but most methods require prohibitively large amounts of data for learning a useful control policy. In this work, we study the integration of robotic reachability priors in actor-critic RL methods for accelerating the learning of MM for reaching and fetching tasks. Namely, we consider the problem of optimal base placement and the subsequent decision of whether to activate the arm for reaching a 6D target. For this, we devise a novel Hybrid RL method that handles discrete and continuous actions jointly, resorting to the Gumbel-Softmax reparameterization. Next, we train a reachability prior using data from the operational robot workspace, inspired by classical methods. Subsequently, we derive Boosted Hybrid RL (BHyRL), a novel algorithm for learning Q-functions by modeling them as a sum of residual approximators. Every time a new task needs to be learned, we can transfer our learned residuals and learn the component of the Q-function that is task-specific, hence, maintaining the task structure from prior behaviors. Moreover, we find that regularizing the target policy with a prior policy yields more expressive behaviors. We evaluate our method in simulation in reaching and fetching tasks of increasing difficulty, and we show the superior performance of BHyRL against baseline methods. Finally, we zero-transfer our learned 6D fetching policy with BHyRL to our MM robot TIAGo++. For more details and code release, please refer to our project site: irosalab.com/rlmmbp

Comparing Social Robot Embodiment for Child Musical Education.

The present research focuses in the comparison of two social robot models running the same Human-Robot Interaction (HRI) applications targeting the context of music education for children aged 9-11, with the objective of underlying the design choices favored by the target audience on the running tasks. The Guitar Tuner consists of two main functionalities: tuning process and performance evaluation, which we implemented using the NAO and Zenbo robots. User evaluation included 20 children and assessed their perceived robot embodiment preferences (e.g., shape, robot motion, displays, and emotional expressivity) and perceived usability aspects. The evaluation used an experimental remote protocol supporting collecting online feedback with users during the COVID-19 pandemic. Empirical results supported performing quantitative and qualitative evaluations of the HRI application and highlighting the perceived differences of robot embodiment features. The discussions center on improving a future version of the HRI application, plus children’s considerations about their preferred robot embodiment features during the observation sessions. Finally, we propose recommendations for robot embodiment design for children and learning based on this case study and discuss protocol limitations during the social distancing context, that we believe as a valid alternative to move forward with experimental designs, particularly in robotics, becoming a great contribution to other researchers facing similar hurdles.

Functional mimicry of Ruffini receptors with fibre Bragg gratings and deep neural networks enables a bio-inspired large-area tactile-sensitive skin

Collaborative robots are expected to physically interact with humans in daily living and the workplace, including industrial and healthcare settings. A key related enabling technology is tactile sensing, which currently requires addressing the outstanding scientific challenge to simultaneously detect contact location and intensity by means of soft conformable artificial skins adapting over large areas to the complex curved geometries of robot embodiments. In this work, the development of a large-area sensitive soft skin with a curved geometry is presented, allowing for robot total-body coverage through modular patches. The biomimetic skin consists of a soft polymeric matrix, resembling a human forearm, embedded with photonic fibre Bragg grating transducers, which partially mimics Ruffini mechanoreceptor functionality with diffuse, overlapping receptive fields. A convolutional neural network deep learning algorithm and a multigrid neuron integration process were implemented to decode the fibre Bragg grating sensor outputs for inference of contact force magnitude and localization through the skin surface. Results of 35 mN (interquartile range 56 mN) and 3.2 mm (interquartile range 2.3 mm) median errors were achieved for force and localization predictions, respectively. Demonstrations with an anthropomorphic arm pave the way towards artificial intelligence based integrated skins enabling safe human–robot cooperation via machine intelligence.

6G and Artificial Intelligence Technologies for Dementia Care: Literature Review and Practical Analysis.

BackgroundThe dementia epidemic is progressing fast. As the world’s older population keeps skyrocketing, the traditional incompetent, time-consuming, and laborious interventions are becoming increasingly insufficient to address dementia patients’ health care needs. This is particularly true amid COVID-19. Instead, efficient, cost-effective, and technology-based strategies, such as sixth-generation communication solutions (6G) and artificial intelligence (AI)-empowered health solutions, might be the key to successfully managing the dementia epidemic until a cure becomes available. However, while 6G and AI technologies hold great promise, no research has examined how 6G and AI applications can effectively and efficiently address dementia patients’ health care needs and improve their quality of life.ObjectiveThis study aims to investigate ways in which 6G and AI technologies could elevate dementia care to address this study gap.MethodsA literature review was conducted in databases such as PubMed, Scopus, and PsycINFO. The search focused on three themes: dementia, 6G, and AI technologies. The initial search was conducted on April 25, 2021, complemented by relevant articles identified via a follow-up search on November 11, 2021, and Google Scholar alerts.ResultsThe findings of the study were analyzed in terms of the interplay between people with dementia’s unique health challenges and the promising capabilities of health technologies, with in-depth and comprehensive analyses of advanced technology-based solutions that could address key dementia care needs, ranging from impairments in memory (eg, Egocentric Live 4D Perception), speech (eg, Project Relate), motor (eg, Avatar Robot Café), cognitive (eg, Affectiva), to social interactions (eg, social robots).ConclusionsTo live is to grow old. Yet dementia is neither a proper way to live nor a natural aging process. By identifying advanced health solutions powered by 6G and AI opportunities, our study sheds light on the imperative of leveraging the potential of advanced technologies to elevate dementia patients’ will to live, enrich their daily activities, and help them engage in societies across shapes and forms.

Design Research of Wearable Soft Avatar Robot for Interactive Social Presence

With the increase in online interaction between distant people, wearable avatar robots are expected to be used in a wide range of daily situations. Soft robotics is highly applicable as a method to achieve this. In this study, we define the design requirements for the daily use of wearable soft avatar robots based on design surveys and cross-field academic research. In addition, we implement prototypes using an inflatable robot and summarize the future issues.

Robotic Embodiment of Human-Like Motor Skills via Reinforcement Learning

Current methods require robots to be reprogrammed for every new task, consuming many engineering resources. This work focuses on integrating real and simulated environments for our proposed “Internet of Skills,” which enables robots to learn advanced skills from a small set of expert demonstrations. By expanding on recent work in the areas of Learning from Demonstrations (LfD) and Reinforcement Learning (RL), we can train robot control policies that can not only effectively complete a given task but also do so with greater performance than the expert demonstrations used to train the policy. In this work, we create simulated environments to train RL algorithms for the task of inverse kinematics and obstacle avoidance. Many state-of-the-art RL algorithms are compared, and we provide a detailed analysis of the state space and parameters chosen. Lastly, we utilize a Vicon motion tracking system and train the robot agent to follow trajectories given by a human operator. Our results show that reinforcement learning algorithms such as proximal policy optimization can develop control policies that are capable of complex control tasks that integrate with the real world, an important first step towards developing a system that can autonomously learn new skills from human demonstrations.