Robotic bodily aware interaction within human environments

Jerbić, Bojan; Stipančić, Tomislav; Tomašić, Tomislav

doi:10.1109/intellisys.2015.7361160

Cited by 7 publications

(5 citation statements)

References 15 publications

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“…For example, a vision modality can be used to strengthen the reasoning hypothesis of the model, as reported in [7]. A similar work has presented a computational architecture that gives the system the ability to adapt to change [8].…”

Section: Current and Comparable Concepts Used For Audio-visual System Perception And Contribution Of Presented Methodologymentioning

confidence: 99%

Person Localization Model Based on a Fusion of Acoustic and Visual Inputs

et al. 2022

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PLEA is an interactive, biomimetic robotic head with non-verbal communication capabilities. PLEA reasoning is based on a multimodal approach combining video and audio inputs to determine the current emotional state of a person. PLEA expresses emotions using facial expressions generated in real-time, which are projected onto a 3D face surface. In this paper, a more sophisticated computation mechanism is developed and evaluated. The model for audio-visual person separation can locate a talking person in a crowded place by combining input from the ResNet network with input from a hand-crafted algorithm. The first input is used to find human faces in the room, and the second input is used to determine the direction of the sound and to focus attention on a single person. After an information fusion procedure is performed, the face of the person speaking is matched with the corresponding sound direction. As a result of this procedure, the robot could start an interaction with the person based on non-verbal signals. The model was tested and evaluated under laboratory conditions by interaction with users. The results suggest that the methodology can be used efficiently to focus a robot’s attention on a localized person.

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Section: Current and Comparable Concepts Used For Audio-visual System Perception And Contribution Of Presented Methodologymentioning

confidence: 99%

Person Localization Model Based on a Fusion of Acoustic and Visual Inputs

et al. 2022

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“…3. The procedure of BN development is described more in detail in [24,25]. For example, at the top of the network, the "Consequence of Events" variable is positioned which has a strong positive influence on the "Desirable for Others" variable.…”

Section: Node (Variable) Consequences Of Events (Ge): Believes About ...mentioning

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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“…Intention-based approaches to human–robot interaction is discussed in [ 26 ]. The social component is also an important aspect of worker–robot interaction [ 27 ]. The way in which a person behaves during the interaction can be analyzed and employed for the purpose of intention recognition.…”

Section: Introductionmentioning

confidence: 99%

Towards a Safe Human–Robot Collaboration Using Information on Human Worker Activity

Orsag

Stipančić

Koren

2023

Sensors

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Most industrial workplaces involving robots and other apparatus operate behind the fences to remove defects, hazards, or casualties. Recent advancements in machine learning can enable robots to co-operate with human co-workers while retaining safety, flexibility, and robustness. This article focuses on the computation model, which provides a collaborative environment through intuitive and adaptive human–robot interaction (HRI). In essence, one layer of the model can be expressed as a set of useful information utilized by an intelligent agent. Within this construction, a vision-sensing modality can be broken down into multiple layers. The authors propose a human-skeleton-based trainable model for the recognition of spatiotemporal human worker activity using LSTM networks, which can achieve a training accuracy of 91.365%, based on the InHARD dataset. Together with the training results, results related to aspects of the simulation environment and future improvements of the system are discussed. By combining human worker upper body positions with actions, the perceptual potential of the system is increased, and human–robot collaboration becomes context-aware. Based on the acquired information, the intelligent agent gains the ability to adapt its behavior according to its dynamic and stochastic surroundings.

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Robotic bodily aware interaction within human environments

Cited by 7 publications

References 15 publications

Person Localization Model Based on a Fusion of Acoustic and Visual Inputs

Person Localization Model Based on a Fusion of Acoustic and Visual Inputs

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

Towards a Safe Human–Robot Collaboration Using Information on Human Worker Activity

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