Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Graph Neural Network to Model Disruption in Human-Aware Robot
  Navigation   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 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).

31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
16
The categories of this paper are: stat
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio