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Real-World Robot Control by Deep Active Inference With a Temporally Hierarchical World Model
Robots in uncertain real-world environments must perform both goal-directed and exploratory actions. However, most deep learning-based control methods neglect exploration and struggle under uncertainty. To address this, we adopt deep active inference, a framework that accounts for human goal-directe
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