Active inference for integrated state-estimation, control, and learning

Conference item

This work presents an approach for control, state-estimation and learning model (hyper)parameters for robotic manipulators. It is based on the active inference framework, prominent in computational neuroscience as a theory of the brain, where behaviour arises from minimizing variational free-energy. First, we show there is a direct relationship between active inference controllers, and classic methods such as PID control. We demonstrate its application for adaptive and robust behaviour of a robotic manipulator that rivals state-of-the-art. Additionally, we show that by learning specific hyperparameters, our approach can deal with unmodeled dynamics, damps oscillations, and is robust against poor initial parameters. The approach is validated on the ‘Franka Emika Panda’ 7 DoF manipulator. Finally, we highlight limitations of active inference controllers for robotic systems.

Authors: Baioumy, M; Duckworth, P; Lacerda, B; Hawes, N

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Host title: Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA)
• Pages: 4665-4671
• Publication date: 2021-10-18
• Event title: 2021 IEEE International Conference on Robotics and Automation (ICRA)
• Event location: Xi'a China / Online
• Event website: https://www.ieee-ras.org/conferences-workshops/fully-sponsored/icra
• Event start date: 2021-05-30
• Event end date: 2021-06-05
• DOI: 10.1109/icra48506.2021.9562009
• EISSN: 2577-087X
• ISSN: 1050-4729
• EISBN: 978-1-7281-9077-8
• ISBN: 978-1-7281-9078-5
• Language: English
• Keywords: FFR