Robotic Emulation of Human Failure Comprehension for Vastly Enhanced Resilience through Metacognition

Introduction

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 malfunctions all by themselves.

Importance of the Project

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.

Future Implications

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.

Proposed Solution

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

Metacognitive awareness is created through a novel knowledge representation that describes how hardware faults may impact robot capabilities.

Metacognitive Planning

Metacognitive planning will yield contingency configurations employing abstract, affordance-based first-order logic planning for self-programming.

Monitoring and Evaluation

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.

Conclusion

This will provide robots with competent strategies to deal with faults in a similar way to humans.