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
|Michael Kamp, Michael Kamp, Irena Koprinska, et. al.
|Published - 2022
|21st European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021 - Virtual, Online
Duration: 13 Sept 2021 → 17 Sept 2021
|Communications in Computer and Information Science
|21st European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021
|13/09/21 → 17/09/21
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