Complex Terrain Navigation via Model Error Prediction

Understand

Robot navigation traditionally relies on building an explicit map that is used to plan collision-free trajectories to a desired target.

• In deformable, complex terrain, using geometric-based approaches can fail to find a path due to mischaracterizing deformable objects as rigid and impassable.
• Instead, we learn to predict an estimate of traversability of terrain regions and to prefer regions that are easier to navigate (e.g., short grass over small shrubs).
• Rather than predicting collisions, we instead regress on realized error compared to a canonical dynamics model.