Graph Neural Networks for Human-Aware Social Navigation

Manso, Luis J.; Jorvekar, Ronit R.; Faria, Diego R.; Bustos, Pablo; Bachiller, Pilar

doi:10.1007/978-3-030-62579-5_12

Cited by 12 publications

(17 citation statements)

References 22 publications

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“…This paper is an extension of [24]. New contributions include considering the speed and previous poses of the robot and the people around it.…”

Section: Abstract Social Navigation • Graph Neural Network • Human-robot Interactionmentioning

confidence: 99%

“…GNNs have been used to model and estimate discomfort in our previous works, [24] and [35]. Both works generate discomfort estimations on 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 on 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 twodimensional 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%

“…The work at hand follows a similar approach to [24] with a number of enhancements. Firstly, we consider two different scores to measure two aspects of social navigation (see Section 3).…”

Section: Graph Neural Network Applied To Human-aware Navigationmentioning

confidence: 99%

“…Firstly, we consider two different scores to measure two aspects of social navigation (see Section 3). Secondly, the model is trained using dynamic scenes where humans and the robot move, which was the main limitation of [24].…”

Section: Graph Neural Network Applied To Human-aware Navigationmentioning

confidence: 99%

See 4 more Smart Citations

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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“…This paper is an extension of [24]. New contributions include considering the speed and previous poses of the robot and the people around it.…”

Section: Abstract Social Navigation • Graph Neural Network • Human-robot Interactionmentioning

confidence: 99%

Section: Graph Neural Network Applied To Human-aware Navigationmentioning

confidence: 99%

Section: Graph Neural Network Applied To Human-aware Navigationmentioning

confidence: 99%

“…The work at hand follows a similar approach to [24] with a number of enhancements. Firstly, we consider two different scores to measure two aspects of social navigation (see Section 3).…”

Section: Graph Neural Network Applied To Human-aware Navigationmentioning

confidence: 99%

Section: Graph Neural Network Applied To Human-aware Navigationmentioning

confidence: 99%

See 3 more Smart Citations

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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SNGNN2D-v2: A GNN-Based Model for the Generation of Human-Aware Cost Maps in Dynamic Environments

Rodriguez-Criado,

Bachiller-Burgos,

Calderita

et al. 2024

Int J of Soc Robotics

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Navigating dynamic, human-populated environments is a critical challenge for mobile robots, as they must balance effective pathfinding with minimizing social disruption. Cost maps can combine information from different nature and are more interpretable than final control signals. This paper addresses the generation of real-time cost maps in human-aware navigation (HAN) by introducing SNGNN2D-v2, a graph neural network designed and trained to capture social interactions and respond to dynamic elements in human-populated environments. SNGNN2D-v2 is evaluated through three types of experiments. The first involves deploying a real robot in a controlled indoor environment and assessing the disturbance caused by the robot when driven by the model. The second experiment tests the proposed model under more complex and unfavorable conditions using simulated environments. Both experiments include a comparison with other proposals using social and navigation metrics. The third experiment compares SNGNN2D-v2 with an end-to-end CNN-based method to evaluate how models generalize across changes in the appearance of the environment and its elements. The results from these experiments suggest that SNGNN2D-v2 is an effective model for human-aware cost map generation for dynamic environments. Its ability to capture dynamic information, generalize across scenarios with different appearances, and represent social interactions could contribute to the development of human-friendly robots.

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Robot Crowd Navigation Incorporating Spatio-Temporal Information Based on Deep Reinforcement Learning

Yang,

Li,

et al. 2024

2024 36th Chinese Control and Decision Conference (CCDC)

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Graph Neural Networks for Human-Aware Social Navigation

Cited by 12 publications

References 22 publications

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

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

SNGNN2D-v2: A GNN-Based Model for the Generation of Human-Aware Cost Maps in Dynamic Environments

Robot Crowd Navigation Incorporating Spatio-Temporal Information Based on Deep Reinforcement Learning

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