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Details of Grant 

EPSRC Reference: EP/R030782/1
Title: Adaptive Robotic EQ for Well-being (ARoEQ)
Principal Investigator: Gunes, Dr H
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Emteq Ltd SoftBank Robotics University of Technology Sydney
Uppsala University
Department: Computer Science and Technology
Organisation: University of Cambridge
Scheme: EPSRC Fellowship
Starts: 15 April 2019 Ends: 14 April 2025 Value (£): 871,445
EPSRC Research Topic Classifications:
Human-Computer Interactions
EPSRC Industrial Sector Classifications:
Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
28 Feb 2018 EPSRC DE and ICT Fellowship Interviews 28 February and 1 March 2018 Announced
11 Jan 2018 EPSRC ICT Prioritisation Panel Jan 2018 Announced
Summary on Grant Application Form
Social robots are envisioned to interact closely with people safely and efficiently, and to add value to people's lives by helping, caring, teaching and entertaining. However, currently there is a major gap between public perception of humanoid / social robot capabilities and their actual capabilities. The cognitive and social capabilities of the current humanoid robots are still very limited.

Although social robotics is an inherently multi-disciplinary field, there are no systematic efforts to develop novel sensing, perception and understanding capabilities for these robots grounded in the state of the art in the fields of affective computing, social signal processing, computer vision and machine learning. To avoid re-inventing the wheel, researchers in HRI often and rightly utilise available sensing / perception tools from other domains, creating their own in-house datasets and evaluations. However, these practices hinder advance in social robotics, leading to a major lack of novel and domain specific tools, and a lack of measures for benchmarking due to a lack of annotated, publicly available multimodal interaction datasets that are vital for comparative evaluation.

This Fellowship aims to address these major gaps in HRI and social robotics. Its vision is to:

(1) equip humanoid robots with novel socio-emotional intelligence and adaptation capabilities grounded in the state of the art in affective computing, social signal processing, computer vision and machine learning fields;

(2) investigate the deployment of humanoid robots as socio-emotionally smart embodied personal devices that can potentially revolutionise our ability to maintain healthier behaviours and working environments, leading to resilient communities.

Key Findings
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Potential use in non-academic contexts
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Summary
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Organisation Website: http://www.cam.ac.uk