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Social Navigation with Adaptive Behavior Sets

Project description

Smart robots to ease the strain of ageing populations

By the year 2050, the population aged over 65 will increase by 60 % worldwide, exerting huge pressure on the healthcare sector and shrinking the productive workforce. Social robots can alleviate this pressure by performing tasks such as bringing items or guiding patients, but must navigate safely and respectfully amongst people. Supported by the Marie Skłodowska-Curie Actions programme, SNABS is developing an intelligent machine-learning system that adapts robot navigation in real-time, adjusting behaviour to fit every environment – from chaotic hospitals to serene offices. Using sophisticated AI, SNABS predicts best moves for robots amidst complex human motion. Tested in healthcare facility trials and subsequently deployed on real robots, this technology holds promise to improve elderly care.

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.

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Topic(s)

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HORIZON-TMA-MSCA-PF-GF - HORIZON TMA MSCA Postdoctoral Fellowships - Global Fellowships

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Call for proposal

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(opens in new window) HORIZON-MSCA-2024-PF-01

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Coordinator

INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET AUTOMATIQUE
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 390 916,86
Address
DOMAINE DE VOLUCEAU ROCQUENCOURT
78153 Le Chesnay Cedex
France

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