RECOVER.ME | Robotic Emulation of Human Failure Comprehension for Vastly Enhanced Resilience through Metacognition

Summary
The aim of the RECOVER.ME project is to achieve human ingenuity in dealing with hardware faults in robotic space exploration. The hypothesis of the project is, that as robots acquire human-like metacognitive
awareness and metacognitive regulatory abilities, they will be enabled to recover from severe but rectifiable hardware malfunction all by themselves. This is of particular importance to planetary exploration, as
a hardware fault need not be the end of a mission. However, as of today, once a hardware malfunction occurs, the remote robot is typically taken out of operation and troubleshooting is done manually. In the
future, especially, when more complex robots are deployed to construct planetary infrastructure for crewed exploration, this can no longer be tolerated. Considering that a hardware fault may occur at any time, such
a situation can become safety-critical for the robot, the established infrastructure, and for astronauts in the vicinity of the robot.

To overcome this issue, RECOVER.ME proposes a novel approach for metacognition-enabled failure handling. Instead of relying on hard-coded recovery strategies by specifying how a robot has to react to a certain sub-system fault, the project aims to bootstrap failure handling as a property of the cognitive architecture of the robot itself. Metacognitive awareness is created through a novel knowledge representation that describes how hardware faults may impact robot capabilities. Metacognitive planning will yield contingency configurations employing abstract, affordance-based first order-logic planning for self-programming. To empower robots to monitor their own programming and evaluate the best strategy to react to arbitrary failure cases, generic limitation models will translate sub-symbolic fault information into semantically interpretable knowledge for metacognitive monitoring and metacognitive evaluation. This will provide robots with competent strategies to deal with faults in a similar way to humans.
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More information & hyperlinks
Web resources: https://cordis.europa.eu/project/id/101116620
Start date: 01-07-2024
End date: 30-06-2029
Total budget - Public funding: 1 499 250,00 Euro - 1 499 250,00 Euro
Cordis data

Original description

The aim of the RECOVER.ME project is to achieve human ingenuity in dealing with hardware faults in robotic space exploration. The hypothesis of the project is, that as robots acquire human-like metacognitive
awareness and metacognitive regulatory abilities, they will be enabled to recover from severe but rectifiable hardware malfunction all by themselves. This is of particular importance to planetary exploration, as
a hardware fault need not be the end of a mission. However, as of today, once a hardware malfunction occurs, the remote robot is typically taken out of operation and troubleshooting is done manually. In the
future, especially, when more complex robots are deployed to construct planetary infrastructure for crewed exploration, this can no longer be tolerated. Considering that a hardware fault may occur at any time, such
a situation can become safety-critical for the robot, the established infrastructure, and for astronauts in the vicinity of the robot.

To overcome this issue, RECOVER.ME proposes a novel approach for metacognition-enabled failure handling. Instead of relying on hard-coded recovery strategies by specifying how a robot has to react to a certain sub-system fault, the project aims to bootstrap failure handling as a property of the cognitive architecture of the robot itself. Metacognitive awareness is created through a novel knowledge representation that describes how hardware faults may impact robot capabilities. Metacognitive planning will yield contingency configurations employing abstract, affordance-based first order-logic planning for self-programming. To empower robots to monitor their own programming and evaluate the best strategy to react to arbitrary failure cases, generic limitation models will translate sub-symbolic fault information into semantically interpretable knowledge for metacognitive monitoring and metacognitive evaluation. This will provide robots with competent strategies to deal with faults in a similar way to humans.

Status

SIGNED

Call topic

ERC-2023-STG

Update Date

02-10-2024
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Horizon Europe
HORIZON.1 Excellent Science
HORIZON.1.1 European Research Council (ERC)
HORIZON.1.1.0 Cross-cutting call topics
ERC-2023-STG ERC STARTING GRANTS
HORIZON.1.1.1 Frontier science
ERC-2023-STG ERC STARTING GRANTS