A context-aware approach in realization of socially intelligent industrial robots

Stipančić, Tomislav; Jerbić, Bojan; Ćurković, Petar

doi:10.1016/j.rcim.2015.07.002

Cited by 18 publications

(6 citation statements)

References 24 publications

(27 reference statements)

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“…This system is an example for robots with supportive behavior for the user but it does not identify many salient features which portray human intention. A situationconscious model is proposed in [70] to improve the design and interactive capabilities of an industrial robot. These findings show that the impact of social and spatial environment have to be considered in order to design a context-aware robot.…”

Section: B Modern Developmentsmentioning

confidence: 99%

Proactive Robots With the Perception of Nonverbal Human Behavior: A Review

2019

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Intelligent robot companions contribute significantly in improving living standards in the modern society. Therefore, human-like decision making skills are sought after during the design of such robots. On the one hand, such features enable the robot to be easily handled by its non-expert human user. On the other hand, the robot will have the capability of dealing with humans without causing any disturbance by the robot's behavior. Mimicing human emotional intelligence is one of the best and reasonable ways of laying the foundation for robotic emotional intelligence. As robots are widely deployed in social environments, perception of the situation or intentions of a user prior to an interaction is required to be proactive. Proactive robots are required to understand what is communicated by the human body language prior to approaching a human. Social constraints in an interaction could be demolished by this assessment in this regard. In this review, we incorporate various findings of human-robot interaction, social robotics and psychophysiology to assess intelligent systems which were capable of evaluating the emotional state of humans prior to an interaction. Second, we identify the cues and evaluation techniques that were utilized by such intelligent agents to simulate and evaluate the suitability of a proactive interaction. Available literature has been evaluated to distinguish limitations of existing methods and suggest possible improvements. These limitations, guiding principles to be adhered to and suggested improvements, are presented as an outcome of the review. INDEX TERMSInteraction initiation, nonverbal cues, context-awareness, social robots, human-robot interaction. A. G. BUDDHIKA P. JAYASEKARA (M'07-SM'17) received the B.Sc. and M.Sc. degrees in electrical engineering from the University of Moratuwa, respectively, in 2004 and 2006, and the doctoral degree in robotics and intelligent systems from Saga University, Japan. He received the Japanese Government Monbukagakusho Scholarship to pursue postgraduate studies in Japan. He joined the Department of Electrical Engineering, University of Moratuwa, after completing his doctoral studies in 2010, where he currently a Senior Lecturer.His main research interests include artificial intelligence techniques, human-robot interaction, human-friendly robotics, machine learning, and intelligent systems. He is the Principle Investigator of the Intelligent Service Robotics Group, Department of Electrical Engineering, University of Moratuwa, and working as the Director of Center for Advanced Robotics (CAR), a multidisciplinary research center of University of Moratuwa from 2017. Currently, he is working on two main projects: intelligent mobile robot for domestic environments (MIRob-Moratuwa Intelligent Robot) and intelligent wheelchair for domestic environments. D. P. CHANDIMA (M'03-SM'17) received the B.Sc. degree from the

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Section: B Modern Developmentsmentioning

confidence: 99%

Proactive Robots With the Perception of Nonverbal Human Behavior: A Review

2019

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“…Through experimentation has been determined that in every injection process, one of the main parts is the determination of the conditions of the control parameters that intervene in the process (pressure, mold temperature and ejection temperature). The research focused on flow behavior tests has been carried out to obtain the basic parameters of the material used (Polymers) and to make predictions of the flow behavior that the injection channels must have to fill the mold cavities completely [50]. In many injection processes, control techniques have been developed in order to allow taking data on the behavior of the tools.…”

Section: State Of the Artmentioning

confidence: 99%

Integration of CAE Modeling and Artificial Intelligence Systems to Support Manufacturing of Plastic Microparts

Rojas

Chaves

Bolivar

et al. 2019

2019 Congreso Internacional De Innovación Y Tendencias en Ingenieria (CONIITI )

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“…But the nature of the environment is chaotic and undefined in terms of degrees of freedom in temporal or spatial dimensions. Some researchers, therefore, tried to decrease the level of uncertainty by constraining the environment [3]. Such approaches often lead to complex, expensive, or inefficient systems that are not adaptable to the current changes occurring within the environment.…”

Section: Introductionmentioning

confidence: 99%

Context-Driven Method in Realization of Optimized Human-Robot Interaction

Koren

Stipančić

Ričko

et al. 2022

Teh. glas. (Online)

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Perceptual uncertainty and environmental volatility are among the most enduring challenges in robotic research today. Contemporary robotic systems are usually designed to work in specific and controlled domains where a total number of variables is defined. Traditional solutions therefore often result in over-constrained interaction spaces or rigid system architectures where any unexpected change can result in system failure. The focus of this work is set on achieving a constant adaptation of the system to changes through interaction. A computational mechanism based on the entropy reduction method is integrated along with the three-component control model. This model is seen as a context-to-data interpreter used to provide context-aware reasoning to the technical system. The mechanism is using a decrease in interaction uncertainties when proofs are provided to the system. In this way, the robot can choose the right interaction strategy that resolves reasoning ambiguities most efficiently

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A context-aware approach in realization of socially intelligent industrial robots

Cited by 18 publications

References 24 publications

Proactive Robots With the Perception of Nonverbal Human Behavior: A Review

Proactive Robots With the Perception of Nonverbal Human Behavior: A Review

Integration of CAE Modeling and Artificial Intelligence Systems to Support Manufacturing of Plastic Microparts

Context-Driven Method in Realization of Optimized Human-Robot Interaction

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