Towards Stochastic Fault-tolerant Control using Precision Learning and Active Inference
This work addresses fault recovery in robotics by offering a more flexible approach, though it appears incremental as it builds on existing active inference methods.
The paper tackles the problem of fault-tolerant control for robotic manipulators by proposing a stochastic scheme based on active inference and precision learning, which eliminates the need for deterministic thresholds and allows gradual sensor exclusion, with experiments showing promising results.
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.