Towards Stochastic Fault-Tolerant Control Using Precision Learning and Active Inference
Mohamed Baioumy (University of Oxford)
Corrado Pezzato (TU Delft - Robust Robot Systems)
Carlos Hernández Corbato (TU Delft - Robust Robot Systems)
Nick Hawes (University of Oxford)
Riccardo Ferrari (TU Delft - Team 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.