A Toolkit to Generate Social Navigation Datasets

Baghel, Rishabh; Kapoor, Aditya; Bachiller, Pilar; Jorvekar, Ronit R.; Rodriguez-Criado, Daniel; Manso, Luis J.

doi:10.1007/978-3-030-62579-5_13

Cited by 5 publications

(6 citation statements)

References 16 publications

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“…However both are still very new and not widely used yet. Another recent work by Baghel et al [329] also proposes the design of simulation frameworks specifically for social navigation, although it focuses more on trajectory simulation than realistic visual input. Similarly, Puig et al [330] presented a framework for modeling human activities with the aim of simulating daily task and potentially training collaborative behaviour [331].…”

Section: Discussionmentioning

confidence: 99%

A Survey on Human-aware Robot Navigation

Möller¹,

Furnari²,

Battiato³

et al. 2021

Preprint

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Intelligent systems are increasingly part of our everyday lives and have been integrated seamlessly to the point where it is difficult to imagine a world without them. Physical manifestations of those systems on the other hand, in the form of embodied agents or robots, have so far been used only for specific applications and are often limited to functional roles (e.g. in the industry, entertainment and military fields). Given the current growth and innovation in the research communities concerned with the topics of robot navigation, human-robot-interaction and human activity recognition, it seems like this might soon change. Robots are increasingly easy to obtain and use and the acceptance of them in general is growing. However, the design of a socially compliant robot that can function as a companion needs to take various areas of research into account. This paper is concerned with the navigation aspect of a socially-compliant robot and provides a survey of existing solutions for the relevant areas of research as well as an outlook on possible future directions.

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Section: Discussionmentioning

confidence: 99%

A Survey on Human-aware Robot Navigation

Möller¹,

Furnari²,

Battiato³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Using DRL, Chen et al (2017) approached the challenge of socially appropriate motion planning by emphasizing what a social robot should not do instead of what it should do . Recently, new toolkits and guidelines for social navigation have been proposed (e.g., Baghel et al, 2020 ; Tsoi et al, 2020a , b ).…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, modern machine learning approaches such as deep reinforcement learning (DRL) have also been utilised for socially compliant navigation. Using DRL, Chen et al (2017) Recently, new toolkits and guidelines for social navigation have been proposed (e.g., Baghel et al, 2020;Tsoi et al, 2020a,b). Researchers have also examined how and when to engage humans appropriately in HRI situations (Walters et al, 2007).…”

Section: Social Appropriateness and Hrimentioning

confidence: 99%

Mind Your Manners! A Dataset and a Continual Learning Approach for Assessing Social Appropriateness of Robot Actions

2022

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To date, endowing robots with an ability to assess social appropriateness of their actions has not been possible. This has been mainly due to (i) the lack of relevant and labelled data and (ii) the lack of formulations of this as a lifelong learning problem. In this paper, we address these two issues. We first introduce the Socially Appropriate Domestic Robot Actions dataset (MANNERS-DB), which contains appropriateness labels of robot actions annotated by humans. Secondly, we train and evaluate a baseline Multi Layer Perceptron and a Bayesian Neural Network (BNN) that estimate social appropriateness of actions in MANNERS-DB. Finally, we formulate learning social appropriateness of actions as a continual learning problem using the uncertainty of Bayesian Neural Network parameters. The experimental results show that the social appropriateness of robot actions can be predicted with a satisfactory level of precision. To facilitate reproducibility and further progress in this area, MANNERS-DB, the trained models and the relevant code are made publicly available at https://github.com/jonastjoms/MANNERS-DB.

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“…-0: unacceptable -20: undesirable -40: acceptable -60: good -80: very good -100: perfect The scenarios compiled in SocNav2 have been generated using SONATA [1]. SONATA is a toolkit built on top of PyRep [18] and CoppeliaSim [36] designed to simulate humanpopulated navigation scenarios and to generate datasets.…”

Section: Socnav2 Datasetmentioning

confidence: 99%

“…The movements of the robot were generated through two different strategies to increase the diversity of its behaviour. The first strategy uses a machine learning model (see [1]) that outputs the control actions of the robot according to a graph representation of the scenario. This model was trained using supervised learning (i.e., it only contains examples of appropriate behaviours), so it has unexpected behaviours in situations that would not usually happen when controlled by humans.…”

Section: Socnav2 Datasetmentioning

confidence: 99%

A graph neural network to model disruption in human-aware robot navigation

Bachiller

Rodriguez-Criado

Jorvekar³

et al. 2021

Multimed Tools Appl

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Autonomous navigation is a key skill for assistive and service robots. To be successful, robots have to minimise the disruption caused to humans while moving. This implies predicting how people will move and complying with social conventions. Avoiding disrupting personal spaces, people’s paths and interactions are examples of these social conventions. This paper leverages Graph Neural Networks to model robot disruption considering the movement of the humans and the robot so that the model built can be used by path planning algorithms. Along with the model, this paper presents an evolution of the dataset SocNav1 (Manso et al 2020) which considers the movement of the robot and the humans, and an updated scenario-to-graph transformation which is tested using different Graph Neural Network blocks. The model trained achieves close-to-human performance in the dataset. In addition to its accuracy, the main advantage of the approach is its scalability in terms of the number of social factors that can be considered in comparison with handcrafted models. The dataset and the model are available in a public repository (https://github.com/gnns4hri/sngnnv2).

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A Toolkit to Generate Social Navigation Datasets

Cited by 5 publications

References 16 publications

A Survey on Human-aware Robot Navigation

A Survey on Human-aware Robot Navigation

Mind Your Manners! A Dataset and a Continual Learning Approach for Assessing Social Appropriateness of Robot Actions

A graph neural network to model disruption in human-aware robot navigation

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