Enabling robotic social intelligence by engineering human social-cognitive mechanisms

Wiltshire, Travis J.; Warta, Samantha F.; Barber, Daniel; Fiore, Stephen M.

doi:10.1016/j.cogsys.2016.09.005

Cited by 32 publications

(22 citation statements)

References 97 publications

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“…Conversely, situations where Type 2 processing is dominant, are those where response time is non-critical and focus on the specific relationships between a relatively small number of observations. These situations are typically unfamiliar, and analytical reasoning is used to identify relationships, to form appropriate behaviours [30,31]. Multiple frameworks exist to implement intelligent computational features to achieve a level of cognition, although there are little consensus and a wide variance in their application and capability.…”

Section: ) Agent Structure and Designmentioning

confidence: 99%

“…There is a close link between stress and frustration, a contributing factor to the perceived task workload [63], as a stressed mental state or a stressful task generate frustration which in turn propagates additional stress. Recent work in the field of social intelligence and cognition aims to use learning techniques to predict emotional states from the observation of actions and behaviours [31,81], which can be used to identify and account for these emotional states.…”

Section: B Human Factors/ergonomicsmentioning

confidence: 99%

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Improving Human-Robot Interaction Utilizing Learning and Intelligence: A Human Factors-Based Approach

Oliff

Liu

Kumar

et al. 2020

IEEE Trans. Automat. Sci. Eng.

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Several decades of development in the fields of robotics and automation has resulted in human-robot-interaction being commonplace, and the subject of intense study. These interactions are particularly prevalent in manufacturing, where human operators have been employed in a number of robotics and automation tasks. The presence of human operators continues to be a source of uncertainty in such systems, despite the study of human factors, in an attempt to better understand these variations in performance. Concurrent developments in intelligent manufacturing present opportunities for adaptability within robotic control. This work examines relevant human factors and develops a framework for integrating the necessary elements of intelligent control and data processing to provide appropriate adaptability to robotic elements, consequently improving collaborative interaction with human colleagues. A neural network-based learning approach is used to predict the influence on human task performance and use these predictions to make informed changes to programmed behaviour, and a methodology developed to further explore the application of learning techniques to this area. The work is supported by an example case-study, in which a simulation model is used to explore the application of the developed system, and its performance in a real-world production scenario. The simulation results reveal that adaptability can be realised with some relatively simple techniques and models if applied in the right manner and that such adaptability is helpful to tackle the issue of performance disparity in manufacturing operations. NTP: This paper presents research into the application of intelligent methodologies to this problem and builds a framework to describe how this information can be captured, generated and used, within manufacturing production processes. This framework helps identify which areas require further research and serves as a basis for the development of a methodology, by which a control system may enable adaptable behaviour to reduce the impact of human performance variation and improve human-machine-interaction. The paper also presents a simulation-based case study, to support the development and evaluate the presented control system on a representative real-world problem. The methodology makes use of a machine learning approach to identify the complex influence of a number of identified human factors on human performance. This knowledge can be used to adjust the robotic behaviour to match the predicted performance of a number of different operators over a number of scenarios. The adaptability reduces performance disparity, reducing idle times and enabling leaner production through WIP reduction. Future work

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Section: ) Agent Structure and Designmentioning

confidence: 99%

Section: B Human Factors/ergonomicsmentioning

confidence: 99%

Improving Human-Robot Interaction Utilizing Learning and Intelligence: A Human Factors-Based Approach

Oliff

Liu

Kumar

et al. 2020

IEEE Trans. Automat. Sci. Eng.

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“…In this section, published works on theoretical studies of robotics models are described. Occurrences of theoretical studies belong only to cognitive architectures and empathy application areas ( In [14], cognitive architectures are described, citing the Learning Intelligent Distribution Agent, Soar, and the Adaptive Control of Thought-Rationale architecture with the aim to provide a set of commitments useful to develop intelligent machines. In this work are presented the Theory of Mind (ToM) and the "perceptual-motor simulation routines", two of the fundamental theories of social cognition.…”

Section: Theoretical Work On the Development Of Robotics Behaviouralmentioning

confidence: 99%

“…interpersonal distance, mental state, user's feedback, and user's profile) [12]. More advanced steps should be performed so that robots are endowed with cognitive and affective capabilities that could provide them with tools to establish empathetic relationships with users and to gain social cognitive mechanisms that are necessary to be perceived as a teammate [13], [14].…”

Section: Introductionmentioning

confidence: 99%

A Survey of Behavioural Models for Social Robots

Nocentini¹,

Fiorini²,

Acerbi³

et al. 2019

Preprint

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The cooperation between humans and robots is becoming increasingly important in our society. Consequently, there is a growing interest in the development of models that can enhance the interaction between humans and robots. A key challenge in the Human-Robot Interaction (HRI) field is to provide robots with cognitive and affective capabilities, developing architectures that let them establish empathetic relationships with users. Several models have been proposed in recent years to solve this open-challenge. This work provides a survey of the most relevant attempts/works. In details, it offers an overview of the architectures present in literature focusing on three specific aspects of HRI: the development of adaptive behavioural models, the design of cognitive architectures, and the ability to establish empathy with the user. The research was conducted within two databases: Scopus and Web of Science. Accurate exclusion criteria were applied to screen the 1007 articles found (at the end 30 articles were selected). For each work, an evaluation of the model is made. Pros and cons of each work are detailed by analysing the aspects that can be improved so that an enjoyable interaction between robots and users can be established.

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“…The first attempts to solve this challenge have been performed by developing intelligent systems able to detect user's emotions [20] and by identifying the key factors that should be adjusted to make the interaction smoother (i.e., interpersonal distance, mental state, user's feedback, and user's profile) [21]. More advanced steps should be performed so that robots are endowed with cognitive and affective capabilities that could provide them with tools to establish empathetic relationships with users and to gain social cognitive mechanisms that are necessary to be perceived as a teammate [22,23].…”

Section: Introductionmentioning

confidence: 99%

A Survey of Behavioral Models for Social Robots

et al. 2019

View full text Add to dashboard Cite

The cooperation between humans and robots is becoming increasingly important in our society. Consequently, there is a growing interest in the development of models that can enhance and enrich the interaction between humans and robots. A key challenge in the Human-Robot Interaction (HRI) field is to provide robots with cognitive and affective capabilities, by developing architectures that let them establish empathetic relationships with users. Over the last several years, multiple models were proposed to face this open-challenge. This work provides a survey of the most relevant attempts/works. In details, it offers an overview of the architectures present in literature focusing on three specific aspects of HRI: the development of adaptive behavioral models, the design of cognitive architectures, and the ability to establish empathy with the user. The research was conducted within two databases: Scopus and Web of Science. Accurate exclusion criteria were applied to screen the 4916 articles found. At the end, 56 articles were selected. For each work, an evaluation of the model is made. Pros and cons of each work are detailed by analyzing the aspects that can be improved to establish an enjoyable interaction between robots and users.

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Enabling robotic social intelligence by engineering human social-cognitive mechanisms

Cited by 32 publications

References 97 publications

Improving Human-Robot Interaction Utilizing Learning and Intelligence: A Human Factors-Based Approach

Improving Human-Robot Interaction Utilizing Learning and Intelligence: A Human Factors-Based Approach

A Survey of Behavioural Models for Social Robots

A Survey of Behavioral Models for Social Robots

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