Variational inference for predictive and reactive controllers

Conference item

Active inference is a general framework for decision-making prominent neuroscience that utilizes variational inference. Recent work in robotics adopted this framework for control and state-estimation; however, these approaches provide a form of ‘reactive’ control which fails to track fast-moving reference trajectories. In this work, we present a variational inference predictive controller. Given a reference trajectory, the controller uses its forward dynamic model to predict future states and chooses appropriate actions. Furthermore, we highlight the limitation of the reactive controller such as the dependency between estimation and control.

Authors: Baioumy, M; Mattamala, M; Hawes, N

• Publication status: Published
• Peer review status: Peer reviewed
• Publication date: 2020-05-31
• Acceptance date: 2020-05-02
• Event title: BRAIN-PIL workshop on New advances in Brain-inspired Perception, Interaction and Learning at IEEE International Conference on Robotics and Automation (ICRA 2020)
• Event location: Online
• Event website: https://brain-pil.github.io/icra2020/
• Event start date: 2020-05-31
• Event end date: 2020-05-31
• Language: English
• Keywords: FFR