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arXiv · 2109.05870

Towards Stochastic Fault-tolerant Control using Precision Learning and Active Inference

Mohamed Baioumy·Corrado Pezzato·Carlos Hernandez Corbato·Nick Hawes·Riccardo Ferrari
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Abstract

This work presents a fault-tolerant control scheme for sensory faults in robotic manipulators based on active inference. In the majority of existing schemes, a binary decision of whether a sensor is healthy (functional) or faulty is made based on measured data. The decision boundary is called a threshold and it is usually deterministic. Following a faulty decision, fault recovery is obtained by excluding the malfunctioning sensor. We propose a stochastic fault-tolerant scheme based on active inference and precision learning which does not require a priori threshold definitions to trigger fault recovery. Instead, the sensor precision, which represents its health status, is learned online in a model-free way allowing the system to gradually, and not abruptly exclude a failing unit. Experiments on a robotic manipulator show promising results and directions for future work are discussed.

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Mohamed Baioumy, Corrado Pezzato, Carlos Hernandez Corbato, Nick Hawes, Riccardo Ferrari. 2021-09-13. Towards Stochastic Fault-tolerant Control using Precision Learning and Active Inference. https://doi.org/10.1007/978-3-030-93736-2_48

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