We present a pixel-based deep Active Inference algorithm (PixelAI) inspired in human body perception and successfully validated in robot body perception and actionas a use case . Our algorithm combines the free energy principle from neuroscience, rooted in variational inference, with deep convolutional decoders to scale the algorithm to directly deal with images input and provide online adaptive inference. The approach enables the robot to perform 1) dynamical body estimation of  arm using only raw monocular camera images and 2) autonomous reaching to "imagined" arm poses in the visual space. We statistically analyzed the algorithm performance in a simulated and a real Nao robot . Results show how the same algorithm deals with both perception an action , modelled as an inference optimization problem .
We present a pixel-based deep active inference algorithm (PixelAI) inspired by human body perception and action . Our algorithm combines the free-energy principle from neuroscience, rooted in variational inference, with deep convolutional decoders to scale the algorithm to directly deal with raw visual input and provide online adaptive inference. Our approach is validated by studying body perception and action in a simulated and a real Nao robot. Results show that our approach allows the robot to perform 1) dynamical body estimation of its arm using only monocular camera images and 2) autonomous reaching to "imagined" arm poses in the visual space. This suggests that robot and human body perception and action can be efficiently solved by viewing both as an active inference problem guided by ongoing sensory input .
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successfully validated in robot body perception and actionas a use case
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free energy
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The approach enables
Our approach is validated by studying body perception and action in a simulated and a real Nao robot. Results show that our approach allows
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We statistically analyzed the algorithm performance in a simulated and a real Nao robot . Results show how the same algorithm deals with both perception an action , modelled as an inference optimization problem
This suggests that robot and human body perception and action can be efficiently solved by viewing both as an active inference problem guided by ongoing sensory input