Using real-time recognition of human-robot interaction styles for creating adaptive robot behaviour in robot-assisted play

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.” DOI: 10.1109/ALIFE.2009.4937693This paper presents an application of the Cascaded Information Bottleneck Method for real-time recognition of Human-Robot Interaction styles in robot-assisted play. This method, that we have developed, is implemented here for an adaptive robot that can recognize and adapt to children's play styles in real time. The robot rewards well-balanced interaction styles and encourages children to engage in the interaction. The potential impact of such an adaptive robot in robot-assisted play for children with autism is evaluated through a study conducted with seven children with autism in a school. A statistical analysis of the results shows the positive impact of such an adaptive robot on the children's play styles and on their engagement in the interaction with the robot

Human-centred design methods : developing scenarios for robot assisted play informed by user panels and field trials

Original article can be found at: http://www.sciencedirect.com/ Copyright ElsevierThis article describes the user-centred development of play scenarios for robot assisted play, as part of the multidisciplinary IROMEC1 project that develops a novel robotic toy for children with special needs. The project investigates how robotic toys can become social mediators, encouraging children with special needs to discover a range of play styles, from solitary to collaborative play (with peers, carers/teachers, parents, etc.). This article explains the developmental process of constructing relevant play scenarios for children with different special needs. Results are presented from consultation with panel of experts (therapists, teachers, parents) who advised on the play needs for the various target user groups and who helped investigate how robotic toys could be used as a play tool to assist in the children’s development. Examples from experimental investigations are provided which have informed the development of scenarios throughout the design process. We conclude by pointing out the potential benefit of this work to a variety of research projects and applications involving human–robot interactions.Peer reviewe

A Systematic Review of Adaptivity in Human-Robot Interaction

As the field of social robotics is growing, a consensus has been made on the design and implementation of robotic systems that are capable of adapting based on the user actions. These actions may be based on their emotions, personality or memory of past interactions. Therefore, we believe it is significant to report a review of the past research on the use of adaptive robots that have been utilised in various social environments. In this paper, we present a systematic review on the reported adaptive interactions across a number of domain areas during Human-Robot Interaction and also give future directions that can guide the design of future adaptive social robots. We conjecture that this will help towards achieving long-term applicability of robots in various social domains

A Pilot Study with a Novel Setup for Collaborative Play of the Humanoid Robot KASPAR with children with autism

This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.This article describes a pilot study in which a novel experimental setup, involving an autonomous humanoid robot, KASPAR, participating in a collaborative, dyadic video game, was implemented and tested with children with autism, all of whom had impairments in playing socially and communicating with others. The children alternated between playing the collaborative video game with a neurotypical adult and playing the same game with the humanoid robot, being exposed to each condition twice. The equipment and experimental setup were designed to observe whether the children would engage in more collaborative behaviours while playing the video game and interacting with the adult than performing the same activities with the humanoid robot. The article describes the development of the experimental setup and its first evaluation in a small-scale exploratory pilot study. The purpose of the study was to gain experience with the operational limits of the robot as well as the dyadic video game, to determine what changes should be made to the systems, and to gain experience with analyzing the data from this study in order to conduct a more extensive evaluation in the future. Based on our observations of the childrens’ experiences in playing the cooperative game, we determined that while the children enjoyed both playing the game and interacting with the robot, the game should be made simpler to play as well as more explicitly collaborative in its mechanics. Also, the robot should be more explicit in its speech as well as more structured in its interactions. Results show that the children found the activity to be more entertaining, appeared more engaged in playing, and displayed better collaborative behaviours with their partners (For the purposes of this article, ‘partner’ refers to the human/robotic agent which interacts with the children with autism. We are not using the term’s other meanings that refer to specific relationships or emotional involvement between two individuals.) in the second sessions of playing with human adults than during their first sessions. One way of explaining these findings is that the children’s intermediary play session with the humanoid robot impacted their subsequent play session with the human adult. However, another longer and more thorough study would have to be conducted in order to better re-interpret these findings. Furthermore, although the children with autism were more interested in and entertained by the robotic partner, the children showed more examples of collaborative play and cooperation while playing with the human adult.Peer reviewe

An emotion and memory model for social robots : a long-term interaction

In this thesis, we investigate the role of emotions and memory in social robotic companions. In particular, our aim is to study the effect of an emotion and memory model towards sustaining engagement and promoting learning in a long-term interaction. Our Emotion and Memory model was based on how humans create memory under various emotional events/states. The model enabled the robot to create a memory account of user's emotional events during a long-term child-robot interaction. The robot later adapted its behaviour through employing the developed memory in the following interactions with the users. The model also had an autonomous decision-making mechanism based on reinforcement learning to select behaviour according to the user preference measured through user's engagement and learning during the task. The model was implemented on the NAO robot in two different educational setups. Firstly, to promote user's vocabulary learning and secondly, to inform how to calculate area and perimeter of regular and irregular shapes. We also conducted multiple long-term evaluations of our model with children at the primary schools to verify its impact on their social engagement and learning. Our results showed that the behaviour generated based on our model was able to sustain social engagement. Additionally, it also helped children to improve their learning. Overall, the results highlighted the benefits of incorporating memory during child-Robot Interaction for extended periods of time. It promoted personalisation and reflected towards creating a child-robot social relationship in a long-term interaction

Emotion and memory model for social robots: a reinforcement learning based behaviour selection

In this paper, we propose a reinforcement learning (RL) mechanism for social robots to select an action based on users’ learning performance and social engagement. We applied this behavior selection mechanism to extend the emotion and memory model, which allows a robot to create a memory account of the user’s emotional events and adapt its behavior based on the developed memory. We evaluated the model in a vocabulary-learning task at a school during a children’s game involving robot interaction to see if the model results in maintaining engagement and improving vocabulary learning across the four different interaction sessions. Generally, we observed positive findings based on child vocabulary learning and sustaining social engagement during all sessions. Compared to the trends of a previous study, we observed a higher level of social engagement across sessions in terms of the duration of the user gaze toward the robot. For vocabulary retention, we saw similar trends in general but also showing high vocabulary retention across some sessions. The findings indicate the benefits of applying RL techniques that have a reward system based on multi-modal user signals or cues

The Future of Humanoid Robots

This book provides state of the art scientific and engineering research findings and developments in the field of humanoid robotics and its applications. It is expected that humanoids will change the way we interact with machines, and will have the ability to blend perfectly into an environment already designed for humans. The book contains chapters that aim to discover the future abilities of humanoid robots by presenting a variety of integrated research in various scientific and engineering fields, such as locomotion, perception, adaptive behavior, human-robot interaction, neuroscience and machine learning. The book is designed to be accessible and practical, with an emphasis on useful information to those working in the fields of robotics, cognitive science, artificial intelligence, computational methods and other fields of science directly or indirectly related to the development and usage of future humanoid robots. The editor of the book has extensive R&D experience, patents, and publications in the area of humanoid robotics, and his experience is reflected in editing the content of the book

Introducing CARESSER: A framework for in situ learning robot social assistance from expert knowledge and demonstrations

Socially assistive robots have the potential to augment and enhance therapist’s effectiveness in repetitive tasks such as cognitive therapies. However, their contribution has generally been limited as domain experts have not been fully involved in the entire pipeline of the design process as well as in the automatisation of the robots’ behaviour. In this article, we present aCtive leARning agEnt aSsiStive bEhaviouR (CARESSER), a novel framework that actively learns robotic assistive behaviour by leveraging the therapist’s expertise (knowledge-driven approach) and their demonstrations (data-driven approach). By exploiting that hybrid approach, the presented method enables in situ fast learning, in a fully autonomous fashion, of personalised patient-specific policies. With the purpose of evaluating our framework, we conducted two user studies in a daily care centre in which older adults affected by mild dementia and mild cognitive impairment (N = 22) were requested to solve cognitive exercises with the support of a therapist and later on of a robot endowed with CARESSER. Results showed that: (i) the robot managed to keep the patients’ performance stable during the sessions even more so than the therapist; (ii) the assistance offered by the robot during the sessions eventually matched the therapist’s preferences. We conclude that CARESSER, with its stakeholder-centric design, can pave the way to new AI approaches that learn by leveraging human–human interactions along with human expertise, which has the benefits of speeding up the learning process, eliminating the need for the design of complex reward functions, and finally avoiding undesired states.Peer ReviewedPostprint (published version