Name
Title | ||
|---|---|---|
Towards Stochastic Fault-Tolerant Control Using Precision Learning and Active Inference |
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-93736-2_48 | MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2021, PT I |
DocType | Volume | ISSN |
Conference | 1524 | 1865-0929 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Baioumy Mohamed | 1 | 0 | 1.69 |
| Corrado Pezzato | 2 | 0 | 0.34 |
| Carlos Hernandez Corbato | 3 | 0 | 0.34 |
| Nick Hawes | 4 | 321 | 34.18 |
| Riccardo Ferrari | 5 | 0 | 0.68 |