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Active Inference

making robotic manipulators adapt autonomously to faults

We are used to see “armies” of robots manufacturing cars or handling packages at sorting facilities. These are very structured environments, with restricted access, where robots can be programmed in a deterministic way. In order to move to unstructured environments, and have them sharing their workspace with humans, we need to account for uncertainties and develop stochastic, adaptive control schemes.

Active inference is a stepping stone we are currently using to address this challenge. Active Inference is prominent in the neuroscientific literature as a general theory of the human brain (Friston, 2010). We can loosely describe it here as the tendency of our brain to both update its own beliefs about the world around us, and act upon it in a way that minimizes “surprise”. In particular, such surprise is defined through the so-called Free Energy and depends on the difference between an internal generative model of the word held by the brain, and sensory information. Several recent approaches in robotics have taken inspiration from it (Buckley et al., 2017) for tasks such as state-estimation (Baioumy et al., 2021), adaptive control (Pezzato et al., 2020), predictive control (Baioumy et al., 2020), human-robot interaction (Ohata & Tani, 2020), reinforcement learning (Tschantz et al., 2020) and many more.

Together with TUD colleagues Corrado Pezzato and Carlos Hernandez Corbato, and Mohamed Bayoumi and Nick Hawes from Oxford Robotics Institute, we recently proposed a novel fault tolerant controller for robotic manipulators based on Active Inference (Pezzato et al., 2020; Baioumy et al., 2021). We are currently working on extending it to learn online the accuracy of its own sensors, which can be used to adapt to natural degradations or complete failures (Baioumy et al., 2021).

Joint work with (mostly): Corrado Pezzato, Carlos Hernandez Corbato, Mohamed Bayoumi and Nick Hawes.

Publications

  1. IEEE_RAL
    A Novel Adaptive Controller for Robot Manipulators based on Active Inference
    Pezzato, Corrado, Ferrari, Riccardo M.G., and Hernández Corbato, Carlos
    IEEE Robotics and Automation Letters 2020
  2. IROS22
    Unbiased Active Inference for Classical Control
    Baioumy, Mohamed, Pezzato, Corrado, Ferrari, Riccardo M.G., and Hawes, Nick
    In International Conference on Intelligent Robots and Systems 2022
  3. SAFEPROCESS22
    Beta Residuals: Improving Fault-Tolerant Control for Sensory Faults via Bayesian Inference and Precision Learning
    Baioumy, Mohamed, Hartemink, William, Ferrari, Riccardo M.G., and Hawes, Nick
    In IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes 2022
  4. IWAI21
    Towards Stochastic Fault-tolerant Control using Precision Learning and Active Inference
    Baioumy, Mohamed, Pezzato, Corrado, Corbato, Carlos Hernández, Hawes, Nick, and Ferrari, Riccardo M.G.
    In International Workshop on Active Inference 2021
  5. ECC21
    Fault-tolerant Control of Robotic Systems with Sensory Faults using Unbiased Active Inference
    Baioumy, Mohamed, Pezzato, Corrado, Ferrari, Riccardo M.G., Corbato, Carlos Hernández, and Hawes, Nick
    In European Control Conference 2021
  6. IWAI20
    Active inference for fault tolerant control of robot manipulators with sensory faults
    Pezzato, Corrado, Baioumy, Mohamed, Corbato, Carlos Hernández, Hawes, Nick, Wisse, Martijn, and Ferrari, Riccardo M.G.
    In International Workshop on Active Inference 2020
  7. ICRA20
    A Novel Adaptive Controller for Robot Manipulators based on Active Inference
    Pezzato, Corrado, Ferrari, Riccardo M.G., and Hernández Corbato, Carlos
    In International Conference on Robotics and Automation 2020

Additional References

  1. The free-energy principle: a unified brain theory?
    Friston, K. J.
    Nature Reviews Neuroscience 2010
  2. The free energy principle for action and perception: A mathematical review
    Buckley, Christopher L, Kim, Chang Sub, McGregor, Simon, and Seth, Anil K
    Journal of Mathematical Psychology 2017
  3. Active Inference for Integrated State-Estimation, Control, and Learning
    Baioumy, Mohamed, Duckworth, Paul, Lacerda, Bruno, and Hawes, Nick
    In Proc of IEEE Int. conference on robotics and automation (ICRA) 2021
  4. Variational Inference for Predictive and Reactive Controllers
    Baioumy, Mohamed, Mattamala, Matias, and Hawes, Nick
    In Workshop on New advances in Brain-inspired Perception, Interaction and Learning 2020
  5. Investigation of the Sense of Agency in Social Cognition, Based on Frameworks of Predictive Coding and Active Inference: A Simulation Study on Multimodal Imitative Interaction
    Ohata, Wataru, and Tani, Jun
    Frontiers in Neurorobotics 2020
  6. Reinforcement learning through active inference
    Tschantz, Alexander, Millidge, Beren, Seth, Anil K, and Buckley, Christopher L
    2020