Towards Stochastic Fault-tolerant Control using Precision Learning and Active Inference

by Corrado Pezzato/

Corrado Pezzato


This work presents a fault-tolerant control scheme for sen-
sory faults in robotic manipulators based on active inference. In the ma-
jority 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 malfunc-
tioning 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 preci-
sion, 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.

  • Control
  • Delft
  • Robotics