2026
Real-World Robot Control by Deep Active Inference With a Temporally Hierarchical World Model
RA-L 2026
Robots in uncertain realworld environments must perform both goaldirected 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-directed