Social Momentum: Design and Evaluation of a Framework for Socially Competent Robot Navigation

Mavrogiannis, Christoforos; Alves‐Oliveira, Patrícia; Thomason, Wil; Knepper, Ross A.

doi:10.1145/3495244

Cited by 33 publications

(15 citation statements)

References 75 publications

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“…In principle, the use of delivery robots in dense urban centers would also benefit from high customer density, with more potential customers living within the area served by a delivery hub (Figliozzi and Jennings, 2020). However, further developments in AI are needed before the deployment of robots in high-density urban centers can be realistically considered (Loke and Rakotonirainy, 2021;Mavrogiannis et al, 2022). Low-density footpaths such as those available in MK make it possible for people to make their way around robots when they stop, as they often do when faced with an uncertain or unexpected situation.…”

Section: Mk-early Demonstratormentioning

confidence: 99%

Humans, robots and artificial intelligences reconfiguring urban life in a crisis

Valdez

Cook

2023

Front. Sustain. Cities

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Autonomous urban robots were introduced in Milton Keynes (MK), UK, in 2018 to automate on-demand grocery delivery. Two years later the COVID-19 pandemic rendered routine activities such as delivering groceries or visiting the supermarket unexpectedly unsafe for humans. The ensuing disruption provided opportunities to investigate the potentialities of robotic and autonomous systems to provide cities with resources for coping with unexpected situations such as pandemics, heatwaves and blizzards and ultimately to transform and reinforce urban flows, leading to new ways of living in the city that arise as a result of emerging human-robot constellations. The crisis accelerated the ongoing transformation in human-robot relationships and made its tensions and potentials visible. The case of MK suggests that the cognitive capabilities of urban AIs are not to be found exclusively in computer bits and human neurons but arise from encounters and contexts, with institutions, policies, practices and even the materiality of the city itself being crucial to the emergence of urban AI.

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Section: Mk-early Demonstratormentioning

confidence: 99%

Humans, robots and artificial intelligences reconfiguring urban life in a crisis

Valdez

Cook

2023

Front. Sustain. Cities

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“…In this work, we focus on inferring three robot performance dimensions relevant to navigation [12]: robot competence, the surprisingness of robot behavior, and clear intent. Robot competence is a popular performance metric [21], especially in robot navigation [22]- [24]. Surprising behavior violates expectations.…”

Section: Related Workmentioning

confidence: 99%

“…We distinguish between explicit and implicit human feedback about robot performance. Explicit feedback corresponds to purposeful or deliberate information conveyed by humans to robots, e.g., through preferences [30], [31] or survey instruments [22], [32]. Meanwhile, implicit feedback are cues and signals that people exhibit without intending to communicate some specific information about robot performance, yet they can be used to infer such perceptions.…”

Section: Related Workmentioning

confidence: 99%

Recent Advances in Mobile Robot Localization in Complex Scenarios

Zhang

Liu

et al. 2023

Lecture Notes in Electrical Engineering

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Human impressions of robot performance are often measured through surveys. As a more scalable and cost-effective alternative, we study the possibility of predicting people's impressions of robot behavior using non-verbal behavioral cues and machine learning techniques. To this end, we first contribute the SEAN TOGETHER Dataset consisting of observations of an interaction between a person and a mobile robot in a Virtual Reality simulation, together with impressions of robot performance provided by users on a 5-point scale. Second, we contribute analyses of how well humans and supervised learning techniques can predict perceived robot performance based on different combinations of observation types (e.g., facial, spatial, and map features). Our results show that facial expressions alone provide useful information about human impressions of robot performance; but in the navigation scenarios we tested, spatial features are the most critical piece of information for this inference task. Also, when evaluating results as binary classification (rather than multiclass classification), the F1-Score of human predictions and machine learning models more than doubles, showing that both are better at telling the directionality of robot performance than predicting exact performance ratings. Based on our findings, we provide guidelines for implementing these predictions models in real-world navigation scenarios.

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“…Game Theory is widely used to model decision-making between rational agents, in economics (Morgenstern and Von Neumann, 1953) and in transport network flow simulation (Figliozzi et al, 2008;Kim and Langari, 2014;Na and Cole, 2014;Talebpour et al, 2015;Flad et al, 2017;Tian et al, 2019). It has also been used in multi agent robotics for coordination tasks (Mavrogiannis and Knepper, 2019;Mavrogiannis et al, 2022). There are fundamental differences between these styles, with economics/transport typically taking an offline, data-driven, explanatory approach while robots require an online, singleshot, real-time decision making approach.…”

Section: Related Workmentioning

confidence: 99%

Unfreezing autonomous vehicles with game theory, proxemics, and trust

Camara¹,

Fox²

2022

Front. Comput. Sci.

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Recent years have witnessed the rapid deployment of robotic systems in public places such as roads, pavements, workplaces and care homes. Robot navigation in environments with static objects is largely solved, but navigating around humans in dynamic environments remains an active research question for autonomous vehicles (AVs). To navigate in human social spaces, self-driving cars and other robots must also show social intelligence. This involves predicting and planning around pedestrians, understanding their personal space, and establishing trust with them. Most current AVs, for legal and safety reasons, consider pedestrians to be obstacles, so these AVs always stop for or replan to drive around them. But this highly safe nature may lead pedestrians to take advantage over them and slow their progress, even to a complete halt. We provide a review of our recent research on predicting and controlling human–AV interactions, which combines game theory, proxemics and trust, and unifies these fields via quantitative, probabilistic models and robot controllers, to solve this “freezing robot” problem.

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Social Momentum: Design and Evaluation of a Framework for Socially Competent Robot Navigation

Cited by 33 publications

References 75 publications

Humans, robots and artificial intelligences reconfiguring urban life in a crisis

Humans, robots and artificial intelligences reconfiguring urban life in a crisis

Recent Advances in Mobile Robot Localization in Complex Scenarios

Unfreezing autonomous vehicles with game theory, proxemics, and trust

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