Objective
The global issue of aging populations (by 2050, a 60% increase in the proportion of persons aged above 65 is projected) places great strain on healthcare systems while simultaneously leading to a decrease in the active labor pool. Social robots have the potential to mitigate the problem by assisting and relieving workers in their basic tasks, such as delivering items or guiding people. To allow this, robots need human-aware navigation to move safely among humans, join, follow, or guide them. However, a major research challenge is the adaptability of the navigation behavior to quickly changing social contexts and environments, such as between areas where more safety space should be given than in others (e.g. hospitals compared to offices). For this purpose, SNABS (Social Navigation with Adaptive Behavior Sets) investigates a machine learning framework (Adaptive Behavior Sets for Transfer Reinforcement Learning) that promises quick adaptations by learning an extensive library of potential behaviors. Being in a new context, it selects the best behavior among them. However, the existing framework suffers from problems hindering its use for real-world applications. This includes the prediction of its behaviors’ outcomes in complex navigation scenarios with human trajectories and the identification of the best behavior in real-time. SNABS aims to overcome these problems by investigating new generative AI models for predicting future outcomes and by introducing new selection strategies for the behaviors. The project further researches how to successfully use the framework to train an adaptive social navigation controller for social robots. The controller will be evaluated in realistic healthcare scenarios. During a final placement at a European robotics manufacturer, the controller will be transferred to their robotic platforms and publicly shared to be directly applicable in the envisioned target scenarios in health and elderly care.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringrobotics
You need to log in or register to use this function
We are sorry... an unexpected error occurred during execution.
You need to be authenticated. Your session might have expired.
Thank you for your feedback. You will soon receive an email to confirm the submission. If you have selected to be notified about the reporting status, you will also be contacted when the reporting status will change.
Keywords
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Funding Scheme
HORIZON-TMA-MSCA-PF-GF - HORIZON TMA MSCA Postdoctoral Fellowships - Global FellowshipsCoordinator
78153 Le Chesnay Cedex
France