By analyzing the geometric properties of diffusion-based trajectory generation, you can detect prediction uncertainty and dynamically adjust action planning horizons without retraining—improving robotic control performance by up to 8.7 percentage points.
This paper introduces GeoAAC, a method that dynamically adjusts how many steps ahead a robot should plan based on task difficulty. Instead of using a fixed planning horizon, it analyzes the geometry of the prediction process to detect when the model is uncertain, then shortens or lengthens the planning window accordingly.