SOURCE-LINKED INTELLIGENCE
WAVE-Go: World-Model Navigation with Adaptive Execution for Wheel-Legged Robots
World models can anticipate the consequences of navigation actions, but predicted action sequences may become invalid during execution, especially when wheel-legged robots encounter dynamic obstacles or change locomotion modes. We propose WAVE-Go, an image-goal navigation framework that separates world-action prediction from interruptible command execution. Its executor adaptively selects an action prefix and cancels pending commands when updated observations invalidate execution. A conditional-risk formulation specifies prefix selection under an estimated cumulative failure budget, while post
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-16T06:30:50.000Z
First collected: 2026-09-20T08:01:03.945Z. This is not the publication date.