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This work presents an approach for control, state-estimation and learningmodel (hyper)parameters for robotic manipulators. It is based on the activeinference framework, prominent in computational neuroscience as a theory of thebrain, where behaviour arises from minimizing variational free-energy. Therobotic manipulator shows adaptive and robust behaviour compared tostate-of-the-art methods. Additionally, we show the exact relationship toclassic methods such as PID control. Finally, we show that by learning atemporal parameter and model variances, our approach can deal with unmodelleddynamics, damps oscillations, and is robust against disturbances and poorinitial parameters. The approach is validated on the `Franka Emika Panda' 7 DoFmanipulator.

Active Inference for Integrated State-Estimation, Control, and Learning