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Found 7 papers, 1 papers with code
The Free Energy Principle for Perception and Action: A Deep Learning Perspective
The free energy principle, and its corollary active inference, constitute a bio-inspired theory that assumes biological agents act to remain in a restricted set of preferred states of the world, i. e., they minimize their free energy.
LatentSLAM: unsupervised multi-sensor representation learning for localization and mapping
Biologically inspired algorithms for simultaneous localization and mapping (SLAM) such as RatSLAM have been shown to yield effective and robust robot navigation in both indoor and outdoor environments.
A learning gap between neuroscience and reinforcement learning
Historically, artificial intelligence has drawn much inspiration from neuroscience to fuel advances in the field.
Deep Active Inference for Autonomous Robot Navigation
Active inference is a theory that underpins the way biological agent's perceive and act in the real world.
Learning Perception and Planning with Deep Active Inference
Active inference is a process theory of the brain that states that all living organisms infer actions in order to minimize their (expected) free energy.
Learning to Catch Piglets in Flight
To develop and test our method, we start with an easy to identify object: a stuffed Piglet.
Bayesian policy selection using active inference
Learning to take actions based on observations is a core requirement for artificial agents to be able to be successful and robust at their task.
