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.
|Title of host publication||Machine Learning and Principles and Practice of Knowledge Discovery in Databases|
|Subtitle of host publication||Proceedings of the International Workshops of ECML PKDD 2021|
|Editors||Michael Kamp, Michael Kamp, Irena Koprinska, et. al.|
|Publication status||Published - 2022|
|Event||21st European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021 - Virtual, Online|
Duration: 13 Sep 2021 → 17 Sep 2021
|Name||Communications in Computer and Information Science|
|Conference||21st European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021|
|Period||13/09/21 → 17/09/21|
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