Generation of Human-Aware Navigation Maps Using Graph Neural Networks

Rodriguez-Criado, Daniel; Bachiller, Pilar; Manso, Luis J.

doi:10.1007/978-3-030-91100-3_2

Cited by 6 publications

(3 citation statements)

References 44 publications

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“…The navigation behaviors observed in the images can be used as targets for socially appropriate navigation behaviours in robots ( Luber et al, 2012 ). The SocNav1 Social Navigation Dataset ( Manso et al, 2020 ) contains different indoor settings with several humans and a robot navigating in the environment, which has been used for learning a map for socially appropriate navigation ( Rodriguez-Criado et al, 2020 ).…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

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

2022

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“…GNNs have been used to model and estimate discomfort in our previous works, [24] and [35]. Both works generate discomfort estimations in a scale from 0 to 100 and consider human-human, human-robot and human-object interactions, as well as walls and other objects.…”

Section: Graph Neural Network Applied To Human-aware Navigationmentioning

confidence: 99%

“…Both works generate discomfort estimations in a scale from 0 to 100 and consider human-human, human-robot and human-object interactions, as well as walls and other objects. While [24] generates a single value a given scenario, [35] generates a two-dimensional cost map using a combination of GNNs and CNNs, in that order. The main limitation of these models is that the scenarios they consider are static (i.e., they disregard human and robot motion).…”

Section: Graph Neural Network Applied To Human-aware Navigationmentioning

confidence: 99%

A Graph Neural Network to Model Disruption in Human-Aware Robot Navigation

Bachiller¹,

Rodriguez-Criado²,

Jorvekar³

et al. 2021

Preprint

Self Cite

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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 [25] 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 closeto-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 1 . Keywords social navigation • graph neural networks • human-robot interactionThis paper is an extension of [24]. New contributions include considering the speed and previous poses of the robot and the people around it. It also provides a new dataset.

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

Bachiller

Rodriguez-Criado

Jorvekar³

et al. 2021

Multimed Tools Appl

Self Cite

View full text Add to dashboard Cite

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).

show abstract

Generation of Human-Aware Navigation Maps Using Graph Neural Networks

Cited by 6 publications

References 44 publications

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

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

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

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