EPSRC Reference: |
EP/R030782/1 |
Title: |
Adaptive Robotic EQ for Well-being (ARoEQ) |
Principal Investigator: |
Gunes, Dr H |
Other Investigators: |
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Researcher Co-Investigators: |
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Project Partners: |
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Department: |
Computer Science and Technology |
Organisation: |
University of Cambridge |
Scheme: |
EPSRC Fellowship |
Starts: |
15 April 2019 |
Ends: |
14 April 2025 |
Value (£): |
871,445
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EPSRC Research Topic Classifications: |
Human-Computer Interactions |
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EPSRC Industrial Sector Classifications: |
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Related Grants: |
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Panel History: |
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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.
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Key Findings |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
Summary |
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Date Materialised |
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Sectors submitted by the Researcher |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Project URL: |
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Further Information: |
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Organisation Website: |
http://www.cam.ac.uk |