Summary
The robots of tomorrow will be endowed with the ability to adapt to drastic and unpredicted changes in their environment including humans.
Such adaptations can however not be boundless: the robot must stay trustworthy, i.e. the adaptations should not be just a recovery
into a degraded functionality. Instead, it must be a true adaptation, meaning that the robot will change its behavior while maintaining
or even increasing its expected performance, and stays at least as safe and robust as before.
RoboSAPIENS will focus on autonomous robotic software adaptations and will lay the foundations for ensuring that such software
adaptations are carried out in an intrinsically safe, trustworthy and efficient manner, thereby reconciling open-ended self-adaptation
with safety by design. RoboSAPIENS will also transform these foundations into 'first time right'-design tools and robotic platforms,
and will validate and demonstrate them up to TRL4.
To achieve this over-all goal, RoboSAPIENS will extend the state of the art in four main objectives.
1. It will enable robotic open-ended self-adaptation in response to unprecedented system structural and environmental changes.
2. It will advance safety engineering techniques to assure robotic safety not only before, during and after adaptation.
3. It will advance deep learning techniques to actively reduce uncertainty in robotic self-adaptation.
4. It will assure trustworthiness of systems that use both deep-learning and computational architectures for robotic self-adaptation.
To realise these objectives, RoboSAPIENS will extend techniques such as MAPE-K (Monitor, Analyze, Plan, Execute, Knowledge) and
Deep Learning to set up generic adaptation procedures and also use an SSH dimension.
RoboSAPIENS will demonstrate this trustworthy robotic self-adaptation on four industry-scale use cases centered around an industrial
disassembly robot, a warehouse robotic swarm, a prolonged hull of an autonomous vessel, and human-robotic interaction.
Such adaptations can however not be boundless: the robot must stay trustworthy, i.e. the adaptations should not be just a recovery
into a degraded functionality. Instead, it must be a true adaptation, meaning that the robot will change its behavior while maintaining
or even increasing its expected performance, and stays at least as safe and robust as before.
RoboSAPIENS will focus on autonomous robotic software adaptations and will lay the foundations for ensuring that such software
adaptations are carried out in an intrinsically safe, trustworthy and efficient manner, thereby reconciling open-ended self-adaptation
with safety by design. RoboSAPIENS will also transform these foundations into 'first time right'-design tools and robotic platforms,
and will validate and demonstrate them up to TRL4.
To achieve this over-all goal, RoboSAPIENS will extend the state of the art in four main objectives.
1. It will enable robotic open-ended self-adaptation in response to unprecedented system structural and environmental changes.
2. It will advance safety engineering techniques to assure robotic safety not only before, during and after adaptation.
3. It will advance deep learning techniques to actively reduce uncertainty in robotic self-adaptation.
4. It will assure trustworthiness of systems that use both deep-learning and computational architectures for robotic self-adaptation.
To realise these objectives, RoboSAPIENS will extend techniques such as MAPE-K (Monitor, Analyze, Plan, Execute, Knowledge) and
Deep Learning to set up generic adaptation procedures and also use an SSH dimension.
RoboSAPIENS will demonstrate this trustworthy robotic self-adaptation on four industry-scale use cases centered around an industrial
disassembly robot, a warehouse robotic swarm, a prolonged hull of an autonomous vessel, and human-robotic interaction.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101133807 |
| Start date: | 01-01-2024 |
| End date: | 31-12-2026 |
| Total budget - Public funding: | 6 883 242,50 Euro - 6 883 233,00 Euro |
Cordis data
Original description
The robots of tomorrow will be endowed with the ability to adapt to drastic and unpredicted changes in their environment including humans.Such adaptations can however not be boundless: the robot must stay trustworthy, i.e. the adaptations should not be just a recovery
into a degraded functionality. Instead, it must be a true adaptation, meaning that the robot will change its behavior while maintaining
or even increasing its expected performance, and stays at least as safe and robust as before.
RoboSAPIENS will focus on autonomous robotic software adaptations and will lay the foundations for ensuring that such software
adaptations are carried out in an intrinsically safe, trustworthy and efficient manner, thereby reconciling open-ended self-adaptation
with safety by design. RoboSAPIENS will also transform these foundations into 'first time right'-design tools and robotic platforms,
and will validate and demonstrate them up to TRL4.
To achieve this over-all goal, RoboSAPIENS will extend the state of the art in four main objectives.
1. It will enable robotic open-ended self-adaptation in response to unprecedented system structural and environmental changes.
2. It will advance safety engineering techniques to assure robotic safety not only before, during and after adaptation.
3. It will advance deep learning techniques to actively reduce uncertainty in robotic self-adaptation.
4. It will assure trustworthiness of systems that use both deep-learning and computational architectures for robotic self-adaptation.
To realise these objectives, RoboSAPIENS will extend techniques such as MAPE-K (Monitor, Analyze, Plan, Execute, Knowledge) and
Deep Learning to set up generic adaptation procedures and also use an SSH dimension.
RoboSAPIENS will demonstrate this trustworthy robotic self-adaptation on four industry-scale use cases centered around an industrial
disassembly robot, a warehouse robotic swarm, a prolonged hull of an autonomous vessel, and human-robotic interaction.
Status
SIGNEDCall topic
HORIZON-CL4-2023-DIGITAL-EMERGING-01-01Update Date
12-03-2024
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Organisations
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| AARHUS UNIVERSITET (AU) (Coördinator)
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| ARISTOTELIO PANEPISTIMIO THESSALONIKIS
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| FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER ANGEWANDTEN FORSCHUNG E.V.
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| INTERNETSIA, S.L. (ISDI)
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| NORGES TEKNISK-NATURVITENSKAPELIGE UNIVERSITET NTNU
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| PAL ROBOTICS SL
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| SIMULA RESEARCH LABORATORY AS
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| TEKNOLOGISK INSTITUT
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| UNIVERSITEIT ANTWERPEN
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| UNIVERSITY OF YORK