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Confidence-Aware Decision-Making and Control for Tool Selection
To evaluate our theoretical account, we framed the decision-making within the tool selection problem, where the agent has to select the best robot arm for a particular control task.
Adaptive Noise Covariance Estimation under Colored Noise using Dynamic Expectation Maximization
The accurate estimation of the noise covariance matrix (NCM) in a dynamic system is critical for state estimation and control, as it has a major influence in their optimality.
Free Energy Principle for the Noise Smoothness Estimation of Linear Systems with Colored Noise
The free energy principle (FEP) from neuroscience provides a framework called active inference for the joint estimation and control of state space systems, subjected to colored noise.
Active Inference in Robotics and Artificial Agents: Survey and Challenges
Active inference is a mathematical framework which originated in computational neuroscience as a theory of how the brain implements action, perception and learning.
