Skill learning framework for human–robot interaction and manipulation tasks

Odesanmi, Gbenga Abiodun; Wang, Qining; Mai, Jingeng

doi:10.1016/j.rcim.2022.102444

Cited by 19 publications

(13 citation statements)

References 32 publications

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“…Recent efforts have concentrated on presenting techniques for improving the adaptiveness of robot systems during HRC in order to enhance HRC potentials [19,20]. Neves and Neto [21] proposed a reinforcement learning approach for assembly sequence planning that included user preferences.…”

Section: Literature Analysismentioning

confidence: 99%

An experimental focus on learning effect and interaction quality in human–robot collaboration

Gervasi

Mastrogiacomo

Franceschini

2023

Prod. Eng. Res. Devel.

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In the landscape of the emerging Industry 5.0, human–robot collaboration (HRC) represents a solution to increase the flexibility and reconfigurability of production processes. Unlike classical industrial automation, in HRC it is possible to have direct interaction between humans and robots. Consequently, in order to effectively implement HRC it is necessary to consider not only technical aspects related to the robot but also human aspects. The focus of this paper is to expand on previous results investigating how the learning process (i.e., the experience gained through the interaction) affects the user experience in the HRC in conjunction with different configuration factors (i.e., robot speed, task execution control, and proximity to robot workspace). Participants performed an assembly task in 12 different configurations and provided feedback on their experience. In addition to perceived interaction quality, self-reported affective state and stress-related physiological indicators (i.e., average skin conductance response and heart rate variability) were collected. A deep quantitative analysis of the response variables revealed a significant influence of the learning process in the user experience. In addition, the perception of some configuration factors changed during the experiment. Finally, a significant influence of participant characteristics also emerged, auguring the necessity of promoting a human-centered HRC.

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Section: Literature Analysismentioning

confidence: 99%

An experimental focus on learning effect and interaction quality in human–robot collaboration

Gervasi

Mastrogiacomo

Franceschini

2023

Prod. Eng. Res. Devel.

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“…[ 14 ] The goal of HRI research is to boost production efficiency and ease the workload by creating cooperative robots that fuse their abilities with human skills. [ 3 ] According to the Institute of Electrical and Electronics Engineers (IEEE), robots are autonomous machines capable of real‐world perception, computation, and action. [ 15 ] Commonly employed robots for interaction and collaboration include robotic arms, AGVs, and chatbots.…”

Section: Overviewmentioning

confidence: 99%

“…[2] The focus of HRI lies in the design of cooperative robots working alongside humans, thereby boosting productivity and mitigating human strain. [3] Technological strides in AI, robotics, soft materials, and bioelectronics have inextricably entwined HRI within the industrial fabric.Integral to the future trajectory of HRI integrated with robotic cognitive intelligence, as depicted in Figure 1, are multimodal communication and regulatory technologies. These technologies cornerstone accurate and resilient perception, cognitive interpretation of environmental cues and human conduct, efficacious humanrobot communication channels, and secure robot control proximate to human presence.…”

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confidence: 99%

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Multimodal Human–Robot Interaction for Human‐Centric Smart Manufacturing: A Survey

Wang,

Zheng,

et al. 2023

Advanced Intelligent Systems

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Human–robot interaction (HRI) has escalated in notability in recent years, and multimodal communication and control strategies are necessitated to guarantee a secure, efficient, and intelligent HRI experience. In spite of the considerable focus on multimodal HRI, comprehensive disquisitions delineating various modalities and intricately analyzing their combinations remain elusive, consequently limiting holistic understanding and future advancements. This article aspires to bridge this inadequacy by conducting a profound exploration of multimodal HRI, predominantly concentrating on four principal modalities: vision, auditory and language, haptics, and physiological sensing. An extensive review encapsulating algorithmic dissection, interface devices, and applicative dimensions forms part of this discourse. This manuscript distinctively combines multimodal HRI with cognitive science, deeply probing into the three dimensions, perception, cognition, and action, thereby demystifying algorithms intrinsic to multimodal HRI. Finally, it accentuates the empirical challenges and contours preemptive trajectories for multimodal HRI in human‐centric smart manufacturing.

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“…[1][2][3][4][5] In order to avoid the inconvenience of traditional bulky electronic devices and human interaction, it is necessary to establish an interactive interface between humans and electronic devices such as electronic sensors, which can capture and analyze external signals in all dimensions and provide high-precision feedback. 6,7 Currently, IWDs cover a large number of wearable types such as wristbands, smart patches, e-textiles and wearable screens, for measuring physical or chemical signals. [8][9][10][11] The electronic sensors can be easily integrated into common processors that can continuously provide accurate and reliable information to the devices.…”

Section: Introductionmentioning

confidence: 99%