Task Engagement as Personalization Feedback for Socially-Assistive Robots and Cognitive Training

Tsiakas, Konstantinos; Abujelala, Maher; Makedon, Fillia

doi:10.3390/technologies6020049

Cited by 59 publications

(54 citation statements)

References 40 publications

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“…Cruz et al ( 2018b ) used multimodal audio-visual input commands along with confidence values to indicate the trustworthiness of the given feedback. Moreover, the use of implicit feedback signals, such as models of user's affective states or measures for their valence or task engagement, have been proposed in the literature (Leite et al, 2011 ; Gordon et al, 2016 ; Ritschel et al, 2017 ; Tsiakas et al, 2018 ).…”

Section: Related Workmentioning

confidence: 99%

Multi-Channel Interactive Reinforcement Learning for Sequential Tasks

et al. 2020

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The ability to learn new tasks by sequencing already known skills is an important requirement for future robots. Reinforcement learning is a powerful tool for this as it allows for a robot to learn and improve on how to combine skills for sequential tasks. However, in real robotic applications, the cost of sample collection and exploration prevent the application of reinforcement learning for a variety of tasks. To overcome these limitations, human input during reinforcement can be beneficial to speed up learning, guide the exploration and prevent the choice of disastrous actions. Nevertheless, there is a lack of experimental evaluations of multi-channel interactive reinforcement learning systems solving robotic tasks with input from inexperienced human users, in particular for cases where human input might be partially wrong. Therefore, in this paper, we present an approach that incorporates multiple human input channels for interactive reinforcement learning in a unified framework and evaluate it on two robotic tasks with 20 inexperienced human subjects. To enable the robot to also handle potentially incorrect human input we incorporate a novel concept for self-confidence, which allows the robot to question human input after an initial learning phase. The second robotic task is specifically designed to investigate if this self-confidence can enable the robot to achieve learning progress even if the human input is partially incorrect. Further, we evaluate how humans react to suggestions of the robot, once the robot notices human input might be wrong. Our experimental evaluations show that our approach can successfully incorporate human input to accelerate the learning process in both robotic tasks even if it is partially wrong. However, not all humans were willing to accept the robot's suggestions or its questioning of their input, particularly if they do not understand the learning process and the reasons behind the robot's suggestions. We believe that the findings from this experimental evaluation can be beneficial for the future design of algorithms and interfaces of interactive reinforcement learning systems used by inexperienced users.

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Section: Related Workmentioning

confidence: 99%

Multi-Channel Interactive Reinforcement Learning for Sequential Tasks

et al. 2020

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“…When it comes to health-related robotic applications, a growing body of research in assistive robots is robot-assisted training [32,33], covering the support of exercises, often combined with gamification elements to increase enjoyment and intrinsic motivation. For example, Schneider et al [25] use a NAO robot to give instructions for isometric exercises.…”

Section: Robotic Companionsmentioning

confidence: 99%

Adaptive linguistic style for an assistive robotic health companion based on explicit human feedback

Ritschel

Seiderer

Janowski

et al. 2019

Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments

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In the future, an increasing amount of social robots will be found in our domestic environments to support and facilitate everyday life. Especially in the context of assistive, health-related support, more and more robotic products are on the way to the consumer market. While one can observe that many commercial efforts are put into the visual appearance, embodiment, motion and sound of companion robots, this paper focuses on the robot's conversational skills. We investigate how to adapt the robot's linguistic style to the individual user's preferences. This includes two forms of robot persona in the context of information retrieval tasks and games, as well as politeness with regard to recommendations. Therefore, we present an autonomous companion robot, which adapts its spoken language based on explicit human feedback. It provides several functionalities for information retrieval, reminders, communication and entertainment as well as health-related recommendations. Results of the in-situ study with elderly participants indicate that human preferences vary with regard to the robot's employed politeness strategies. Furthermore, the participants preferred assistant persona over companion persona in the information retrieval context. CCS CONCEPTS • Computing methodologies → Intelligent agents; Reinforcement learning; • Human-centered computing → Haptic devices;

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“…in exercises/games), human social signals can be used, including smile and gaze [8,12,21], laughter [35], tactile [2] or prosodic [17] feedback, interaction distance, gaze meeting, motion speed timing [23], gesture and posture [24,27], or gaze direction [6]. Another option is to use physiological data from ECG [22] or EEG [34].…”

Section: Adaptive Social Robotsmentioning

confidence: 99%

Drink-O-Mender

Ritschel

Seiderer

Janowski

et al. 2018

Proceedings of the 3rd International Workshop on Multisensory Approaches to Human-Food Interaction

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Social robots become increasingly important in the domain of healthcare and maintenance. Nutrition is not an exception: research robots are used in several experiments to teach people about healthy nutrition. Moreover, robotic products, whose task is to keep track of the user's nutrition, to provide tips, reminders, and to recognize anomalies in health-related behaviors are on the way to market. To convince users of a robot's recommendations, speech is an important interaction modality. However, automatically adapting a robot's spoken advise depending on users' behaviors is still a challenge. We address this issue by building Drink-O-Mender, an interactive installation, which includes a Reeti robot augmented with additional sensing and adaptation abilities. The robot is designed to offer drinks in a social setting. It aims to convince users of consuming healthy drinks while adapting its spoken advices depending on the users' selected beverage choices. The installation is equipped with custom hardware including a smartscale to sense the type and quantity of consumed drinks. We describe the interactive installation in detail and demonstrate feasibility of generating adaptive spoken advices by reporting on insights gained from exhibiting the installation during a public event and observing interactions of 78 users with the robot.

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Task Engagement as Personalization Feedback for Socially-Assistive Robots and Cognitive Training

Cited by 59 publications

References 40 publications

Multi-Channel Interactive Reinforcement Learning for Sequential Tasks

Multi-Channel Interactive Reinforcement Learning for Sequential Tasks

Adaptive linguistic style for an assistive robotic health companion based on explicit human feedback

Drink-O-Mender

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