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Towards Stochastic Fault-Tolerant Control Using Precision Learning and Active Inference

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Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021)

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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Correspondence to Mohamed Baioumy .

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Baioumy, M., Pezzato, C., Corbato, C.H., Hawes, N., Ferrari, R. (2021). Towards Stochastic Fault-Tolerant Control Using Precision Learning and Active Inference. In: Kamp, M., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2021. Communications in Computer and Information Science, vol 1524. Springer, Cham. https://doi.org/10.1007/978-3-030-93736-2_48

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  • DOI: https://doi.org/10.1007/978-3-030-93736-2_48

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