How Should a Robot React Before People's Touch?: Modeling a Pre-Touch Reaction Distance for a Robot's Face

Shiomi, Masahiro; Shatani, Kodai; Minato, Takashi; Ishiguro, Hiroshi

doi:10.1109/lra.2018.2856303

Cited by 20 publications

(22 citation statements)

References 41 publications

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“…The average pre-touch distance for all 20 participants was 17.93 cm (SE = 1.98 cm). The average pre-touch distance obtained for the physical space in our previous work was 20 cm [6].…”

Section: Data Overviewmentioning

confidence: 82%

“…In our previous work, we investigated the pre-touch distance in both human-human and human-robot interactions in the physical space and its application for enhancing human-robot social interactions [6]. However, it is still not clear whether the pre-touch distance measured in the physical space can be applied for virtual agents.…”

Section: Related Workmentioning

confidence: 99%

“…However, prior research has focused mainly on the touch and after-touch interactions of virtual agents with participants, with no consideration of the factors involved in the before-touch interaction. One study has revealed that if the reaction distance of a robot before being touched is similar to that of humans, this behavior can help to convey a more natural and human-like impression [6]. In the VR context, simulating interactions between objects and agents by considering physical phenomenon has been studied [1], however, here also no consideration was given to the reaction distance before being touched.…”

Section: Introductionmentioning

confidence: 99%

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Modeling of Pre-Touch Reaction Distance for Faces in a Virtual Environment

Mejía

Saito

Kimoto

et al. 2021

Journal of Information Processing

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One of the most common cues in human relations is the reaction when someone approaches for a touch interaction. While the "before touch" distance has been investigated in daily life scenarios, it has not been studied for virtual environments. The measurements of a pre-touch distance in virtual reality can be applied to study social interactions especially for haptic interactions in virtual spaces where virtual agents interact autonomously with human participants. In the first stage of this study, we collected data to define a pre-touch distance when a virtual agent tries to touch the participant's face. On the basis of these results, we then classified participants into two groups based on preferred pre-touch distance: a "Near" group and a "Far" group. Next, we experimentally investigated the relationship between the participant's perception of an avatar's reaction to touch interaction and their preferred pre-touch distance. The results indicated that the participants felt friendliness to the agent who reacts with shorter pre-touch distance. We also found that the participant's pre-touch distance defined their preferences regarding the agent's behavior: those with a shorter pre-touch distance preferred agents with a closer interaction distance, and those with a longer pre-touch distance preferred agents with a longer interaction distance.

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“…The average pre-touch distance for all 20 participants was 17.93 cm (SE = 1.98 cm). The average pre-touch distance obtained for the physical space in our previous work was 20 cm [6].…”

Section: Data Overviewmentioning

confidence: 82%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling of Pre-Touch Reaction Distance for Faces in a Virtual Environment

Mejía

Saito

Kimoto

et al. 2021

Journal of Information Processing

Self Cite

View full text Add to dashboard Cite

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“…Many social robots also consider such position relationships before interacting with people as preconversational interactions (Obaid et al, 2016;Li et al, 2019;Saunderson and Nejat, 2019). Following such concepts, robotics researchers investigated the pre-touch reaction distance around a face between people and reported that the average distance is about 20 cm (Shiomi et al, 2018). They developed an android robot that reacted to the touch behaviors of people and concluded that a 20-cm threshold as a pre-touch reaction distance is more natural and human-like than personal space as a pre-touch reaction distance.…”

Section: Technical Challenges For Touch Interactionmentioning

confidence: 99%

Technical Challenges for Smooth Interaction With Seniors With Dementia: Lessons From Humanitude™

et al. 2021

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Due to cognitive and socio-emotional decline and mental diseases, senior citizens, especially people with dementia (PwD), struggle to interact smoothly with their caregivers. Therefore, various care techniques have been proposed to develop good relationships with seniors. Among them, Humanitude is one promising technique that provides caregivers with useful interaction skills to improve their relationships with PwD, from four perspectives: face-to-face interaction, verbal communication, touch interaction, and helping care receivers stand up (physical interaction). Regardless of advances in elderly care techniques, since current social robots interact with seniors in the same manner as they do with younger adults, they lack several important functions. For example, Humanitude emphasizes the importance of interaction at a relatively intimate distance to facilitate communication with seniors. Unfortunately, few studies have developed an interaction model for clinical care communication. In this paper, we discuss the current challenges to develop a social robot that can smoothly interact with PwDs and overview the interaction skills used in Humanitude as well as the existing technologies.

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“…Robots allow long operation hours on repetitive tasks and provide consistent and precise performance beyond human capability, while human operators contribute improved situational awareness, experienced and intuitive decision making, and the ability to work around unexpected situations. While research on human-robot interaction has gained a lot of momentum in recent years [1]- [3], MH-MR systems are a relatively new area involving interaction and collaboration between multiple humans and robots.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Workload Allocation for Multi-Human Multi-Robot Teams for Independent and Homogeneous Tasks

Mina

Kannan

et al. 2020

IEEE Access

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Multi-human multi-robot (MH-MR) systems have the ability to combine the potential advantages of robotic systems with those of having humans in the loop. Robotic systems contribute precision performance and long operation on repetitive tasks without tiring, while humans in the loop improve situational awareness and enhance decision-making abilities. A system's ability to adapt allocated workload to changing conditions and the performance of each individual (human and robot) during the mission is vital to maintaining overall system performance. Previous works from literature including market-based and optimization approaches have attempted to address the task/workload allocation problem with focus on maximizing the system output without regarding individual agent conditions, lacking in real-time processing and have mostly focused exclusively on multi-robot systems. Given the variety of possible combination of teams (autonomous robots and human-operated robots: any number of human operators operating any number of robots at a time) and the operational scale of MH-MR systems, development of a generalized framework of workload allocation has been a particularly challenging task. In this paper, we present such a framework for independent homogeneous missions, capable of adaptively allocating the system workload in relation to health conditions and work performances of human-operated and autonomous robots in real-time. The framework consists of removable modular function blocks ensuring its applicability to different MH-MR scenarios. A new workload transition function block ensures smooth transition without the workload change having adverse effects on individual agents. The effectiveness and scalability of the system's workload adaptability is validated by experiments applying the proposed framework in a MH-MR patrolling scenario with changing human and robot condition, and failing robots.

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How Should a Robot React Before People's Touch?: Modeling a Pre-Touch Reaction Distance for a Robot's Face

Cited by 20 publications

References 41 publications

Modeling of Pre-Touch Reaction Distance for Faces in a Virtual Environment

Modeling of Pre-Touch Reaction Distance for Faces in a Virtual Environment

Technical Challenges for Smooth Interaction With Seniors With Dementia: Lessons From Humanitude™

Adaptive Workload Allocation for Multi-Human Multi-Robot Teams for Independent and Homogeneous Tasks

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