Towards a dynamic emotional model

Godfrey, W. Wilfred; Nair, Shivashankar B.; Hwa, Kim Dong

doi:10.1109/isie.2009.5217935

Cited by 14 publications

(18 citation statements)

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“…We considered the first human as the experiment subject and the second human as the control subject. Based on the human-human study, we found the following major task situations/contexts/events and the corresponding affects, as given in Table 2 [45][46][47][48]. Table 2 shows the major events that occurred during the human-human collaboration and the ways the humans, especially the first humans, expressed the situations/events through affective expressions.…”

Section: Inspiration From a Human-human Collaborative Task: The Biomi...mentioning

confidence: 99%

“…We called this test the affect recognition test. To implement the HRC of Figure 3, we just replaced the first human of Figure 4 with a robot, selected a slightly more representative task instead of the simple task of Figure 4, and enabled the robot to express various affective expressions similar to that the first human did at different events [45][46][47][48].…”

Section: Inspiration From a Human-human Collaborative Task: The Biomi...mentioning

confidence: 99%

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Dynamic Affect-Based Motion Planning of a Humanoid Robot for Human-Robot Collaborative Assembly in Manufacturing

Rahman

2024

Electronics

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The objective was to investigate the impacts of the robot’s dynamic affective expressions in task-related scenarios on human–robot collaboration (HRC) and performance in human–robot collaborative assembly tasks in flexible manufacturing. A human–robot hybrid cell was developed to facilitate a human co-worker and a robot to collaborate to assemble a few parts into a final product. The collaborative robot was a humanoid manufacturing robot with the ability to display its affective states due to changes in task scenarios on its face. The assembly task was divided into several subtasks, and based on an optimization strategy, the subtasks were optimally allocated to the human and the robot. A computational model of the robot’s affective states was derived inspired by that of humans following the biomimetic approach, and an affect-based motion planning strategy for the robot was proposed to enable the robot to adjust its motions and behaviors with task situations and communicate (inform) the situations to the human co-worker through affective expressions. The HRC and the assembly performance for the affect-based motion planning were experimentally evaluated based on a comprehensive evaluation scheme and were compared with two alternative conditions: (i) motion planning that did not display affective states, and (ii) motion planning that displayed text messages instead of displaying affective states to communicate the situations to the human co-worker. The results clearly showed that the dynamic affect-based motion planning produced significantly better HRC and assembly performance than that produced by motion planning associated with the display of no affective states or text messages. The results encouraged employing manufacturing robots with dynamic affective expressions to collaborate with humans in flexible assembly in manufacturing to improve HRC and assembly performance.

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Section: Inspiration From a Human-human Collaborative Task: The Biomi...mentioning

confidence: 99%

Section: Inspiration From a Human-human Collaborative Task: The Biomi...mentioning

confidence: 99%

Dynamic Affect-Based Motion Planning of a Humanoid Robot for Human-Robot Collaborative Assembly in Manufacturing

Rahman

2024

Electronics

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“…In addition to challenges in comparison between publications, it is often not possible to compare each system to anything outside of the proposed control from within the study. In our review we found only one paper that compared their emotional model with a baseline system [61], every other publication compared only one emotional model usually to a control group which had no emotion added.…”

Section: A Paradigm Features 1) Distinct Approaches For Interaction A...mentioning

confidence: 99%

A Survey of Robotics and Emotion: Classifications and Models of Emotional Interaction

Savery¹,

Weinberg²

2020

Preprint

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As emotion plays a growing role in robotic research it is crucial to develop methods to analyze and compare among the wide range of approaches. To this end we present a survey of 1427 IEEE and ACM publications that include robotics and emotion. This includes broad categorizations of trends in emotion input analysis, robot emotional expression, studies of emotional interaction and models for internal processing. We then focus on 232 papers that present internal processing of emotion, such as using a humans emotion for better interaction or turning environmental stimuli into an emotional drive for robotic path planning. We conducted constant comparison analysis of the 232 papers and arrived at three broad categorization metrics -emotional intelligence, emotional model and implementation -each including two or three subcategories. The subcategories address the algorithm used, emotional mapping, history, the emotional model, emotional categories, the role of emotion, the purpose of emotion and the platform. Our results show a diverse field of study, largely divided by the role of emotion in the system, either for improved interaction, or improved robotic performance. We also present multiple future opportunities for research and describe intrinsic challenges common in all publications.

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“…What is used are terms like 'emotion' or 'feeling' without the proper knowledge of the scientific field defining those terms (see eg. [8] or [9]). The terms are taken from theoretical work, often mixed together from different theories and used without proper check for interoperability.…”

Section: B Interdisciplinary Approachmentioning

confidence: 99%

A decision unit inspired by cognitive sciences for future robotic applications

2011

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Abstract-The complexity of technical systems is constantly increasing. To manage the amount of data several strategies have been followed and a lot of achievements were made especially in artificial intelligence. But still the capabilities of human intelligence are unrivaled. The contribution of this article is an introduction to a decision unit based on psychoanalytical models and a description how the findings can be used in future robotic applications. The neuro-psychoanalytical theories used, cover a comprehensive model of human intelligence and presents structures for all aspects of human decision making. The psychoanalytic-inspired decision unit shows promising prospects that standard planning and control units lack in various respects. Amongst others, scenario and experience-based planning, drivebased intention models, and the 3-tier (neuro-symbolic, thing presentations, word presentations) data processing architecture, are very promising methods. In order to validate the model, a simulation environment was implemented holding various agents with the neuro-psychoanalytical decision unit. This first implementation shows the possibilities of the system and gives a great outlook for future investigations of the capabilities.

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Towards a dynamic emotional model

Cited by 14 publications

References 14 publications

Dynamic Affect-Based Motion Planning of a Humanoid Robot for Human-Robot Collaborative Assembly in Manufacturing

Dynamic Affect-Based Motion Planning of a Humanoid Robot for Human-Robot Collaborative Assembly in Manufacturing

A Survey of Robotics and Emotion: Classifications and Models of Emotional Interaction

A decision unit inspired by cognitive sciences for future robotic applications

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