  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.
