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cs.RO2025
Real-World Robot Control by Deep Active Inference With a Temporally Hierarchical World Model
Kentaro Fujii, Shingo Murata
Robots in uncertain real-world environments must perform both goal-directed and exploratory actions. However, most deep learning-based control methods neglect exploration and strug…
cs.RO2025
Deep Active Inference with Diffusion Policy and Multiple Timescale World Model for Real-World Exploration and Navigation
Riko Yokozawa, Kentaro Fujii, Yuta Nomura +1
Autonomous robotic navigation in real-world environments requires exploration to acquire environmental information as well as goal-directed navigation in order to reach specified t…