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WAM: Training a world model with quadruped robot locomotion data for navigation planning

WAM trains a forward-dynamics world model on quadruped robot locomotion data collected under convex model predictive control, replaces hand-crafted steering laws with receding-horizon sampling planning, and combines A* global paths with dynamic obstacle extrapolation to achieve navigation and obstacle avoidance. The material states that all numerical values come from local reproduction experiments.

Part of the event: WAM: Training a World Model with Quadruped Locomotion Data for Navigation Planning

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