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Found 7 papers, 4 papers with code
Guessing human intentions to avoid dangerous situations in caregiving robots
This is particularly important for social robots designed for human care, which may face potentially dangerous situations for people, such as unseen obstacles in their way, that should be avoided.
SocNavGym: A Reinforcement Learning Gym for Social Navigation
We propose SocNavGym, an advanced simulation environment for social navigation that can generate a wide variety of social navigation scenarios and facilitates the development of intelligent social agents.
Multi-person 3D pose estimation from unlabelled data
Its numerous applications make multi-human 3D pose estimation a remarkably impactful area of research.
A Graph Neural Network to Model Disruption in Human-Aware Robot Navigation
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.
Generation of Human-aware Navigation Maps using Graph Neural Networks
Minimising the discomfort caused by robots when navigating in social situations is crucial for them to be accepted.
Multi-camera Torso Pose Estimation using Graph Neural Networks
The proposal presented in this paper makes use of graph neural networks to merge the information acquired from multiple camera sources, achieving a mean absolute error below 125 mm for the location and 10 degrees for the orientation using low-resolution RGB images.
Graph Neural Networks for Human-aware Social Navigation
Autonomous navigation is a key skill for assistive and service robots.
