Learning Proxemics for Personalized Human–Robot Social Interaction

Patompak, Pakpoom; Jeong, Sungmoon; Nilkhamhang, Itthisek; Chong, Nak Young

doi:10.1007/s12369-019-00560-9

Cited by 42 publications

(28 citation statements)

References 23 publications

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“…Specifically, if we employ the model (Patompak et al, 2019 ) presented in section 3.3 as the learning component in our framework, the personalized size and shape of the personal zone can in fact improve the social intelligence of the robot. By avoiding crossing the comfort zone of people, these robots can learn to plan paths without disturbing the visitors of the shopping mall while performing the cleaning task.…”

Section: Case Studies and Discussionmentioning

confidence: 99%

“…The purpose of analyzing this approach is to demonstrate that biased behaviors can also be learned from biased demonstrations or observations. We analyze the approach proposed by Patompak et al ( 2019 ) to predict personalized proxemics areas that correspond to the characteristics of individual people. This approach generates personalized comfort zones of a specific size and shape by associating the personal area with the activity that a person performs or characteristics of the person.…”

Section: Learning—relearning Framework For Socially-aware Robot Namentioning

confidence: 99%

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From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation

2021

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The exponentially increasing advances in robotics and machine learning are facilitating the transition of robots from being confined to controlled industrial spaces to performing novel everyday tasks in domestic and urban environments. In order to make the presence of robots safe as well as comfortable for humans, and to facilitate their acceptance in public environments, they are often equipped with social abilities for navigation and interaction. Socially compliant robot navigation is increasingly being learned from human observations or demonstrations. We argue that these techniques that typically aim to mimic human behavior do not guarantee fair behavior. As a consequence, social navigation models can replicate, promote, and amplify societal unfairness, such as discrimination and segregation. In this work, we investigate a framework for diminishing bias in social robot navigation models so that robots are equipped with the capability to plan as well as adapt their paths based on both physical and social demands. Our proposed framework consists of two components: learning which incorporates social context into the learning process to account for safety and comfort, and relearning to detect and correct potentially harmful outcomes before the onset. We provide both technological and societal analysis using three diverse case studies in different social scenarios of interaction. Moreover, we present ethical implications of deploying robots in social environments and propose potential solutions. Through this study, we highlight the importance and advocate for fairness in human-robot interactions in order to promote more equitable social relationships, roles, and dynamics and consequently positively influence our society.

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Section: Case Studies and Discussionmentioning

confidence: 99%

Section: Learning—relearning Framework For Socially-aware Robot Namentioning

confidence: 99%

From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation

2021

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“…Our proposed social navigation system is aimed at considering all DILMO dimensions and proxemic zones for individuals and groups of people. [19] x x x -Individual Personal C. Mavrogiannis et al [52] --x -Group -C. Lobato et al [55] x --x Individual Intimate, Personal E. Avrunin et al [29] x x x x Individual Personal, Social K. Zheng et al [12] x x -Both Personal Maja Pantic et al [51] ------A. Vega-Magro et al [61] x -x -Group 4 zones H. Khambhaita et al [57] x -x --Mead et al [58] x --x --D. Tokmurzina et al [31] x ---Individual -J. Han and I. Bae [60] x x -x --P. Patompak et al [62] x --x Individual 4 zones…”

Section: Related Workmentioning

confidence: 99%

“…Another approach to model the social space is proposed in [ 62 ]. Authors present a new definition of social space, named as Dynamic Social Force (

).…”

Section: Related Workmentioning

confidence: 99%

An Approach of Social Navigation Based on Proxemics for Crowded Environments of Humans and Robots

et al. 2021

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Nowadays, mobile robots are playing an important role in different areas of science, industry, academia and even in everyday life. In this sense, their abilities and behaviours become increasingly complex. In particular, in indoor environments, such as hospitals, schools, banks and museums, where the robot coincides with people and other robots, its movement and navigation must be programmed and adapted to robot–robot and human–robot interactions. However, existing approaches are focused either on multi-robot navigation (robot–robot interaction) or social navigation with human presence (human–robot interaction), neglecting the integration of both approaches. Proxemic interaction is recently being used in this domain of research, to improve Human–Robot Interaction (HRI). In this context, we propose an autonomous navigation approach for mobile robots in indoor environments, based on the principles of proxemic theory, integrated with classical navigation algorithms, such as ORCA, Social Momentum, and A*. With this novel approach, the mobile robot adapts its behaviour, by analysing the proximity of people to each other, with respect to it, and with respect to other robots to decide and plan its respective navigation, while showing acceptable social behaviours in presence of humans. We describe our proposed approach and show how proxemics and the classical navigation algorithms are combined to provide an effective navigation, while respecting social human distances. To show the suitability of our approach, we simulate several situations of coexistence of robots and humans, demonstrating an effective social navigation.

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“…Their experiments with participants in a search and rescue scenario and followed by a questionnaire showed a preference for a logarithmic proxemic scaling function. Patompak et al [63] developed an inference method to learn human proxemic preferences. Their method is based on the social force model and reinforcement learning.…”

Section: Proxemics In Human-robot Interactionsmentioning

confidence: 99%

Space Invaders: Pedestrian Proxemic Utility Functions and Trust Zones for Autonomous Vehicle Interactions

Camara

Fox

2020

Int J of Soc Robotics

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Understanding pedestrian proxemic utility and trust will help autonomous vehicles to plan and control interactions with pedestrians more safely and efficiently. When pedestrians cross the road in front of human-driven vehicles, the two agents use knowledge of each other’s preferences to negotiate and to determine who will yield to the other. Autonomous vehicles will require similar understandings, but previous work has shown a need for them to be provided in the form of continuous proxemic utility functions, which are not available from previous proxemics studies based on Hall’s discrete zones. To fill this gap, a new Bayesian method to infer continuous pedestrian proxemic utility functions is proposed, and related to a new definition of ‘physical trust requirement’ (PTR) for road-crossing scenarios. The method is validated on simulation data then its parameters are inferred empirically from two public datasets. Results show that pedestrian proxemic utility is best described by a hyperbolic function, and that trust by the pedestrian is required in a discrete ‘trust zone’ which emerges naturally from simple physics. The PTR concept is then shown to be capable of generating and explaining the empirically observed zone sizes of Hall’s discrete theory of proxemics.

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Learning Proxemics for Personalized Human–Robot Social Interaction

Cited by 42 publications

References 23 publications

From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation

From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation

An Approach of Social Navigation Based on Proxemics for Crowded Environments of Humans and Robots

Space Invaders: Pedestrian Proxemic Utility Functions and Trust Zones for Autonomous Vehicle Interactions

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