A small wheeled robot and a larger robot with a different steering system cannot move through a narrow corridor in exactly the same way. The command to turn right produces different motions. A team led by Zhidong Wang proposed teaching one program to plan paths for several kinds of robots while still respecting those differences.

The method uses a world model^[1]: software that predicts how the scene around a robot may change after it moves. But pictures alone are not enough. The researchers also describe how each particular robot accelerates and turns. The planner can therefore consider what the machine can actually do, not merely which space looks empty.

In computer experiments, the researchers used seven robots representing five kinds of movement. The shared program succeeded 3.8 to 10.6 percentage points^[2] more often than some comparison methods. They then drove a real robot manually for three minutes to learn how it moved, before testing navigation in an unfamiliar indoor space.

That real-world test matters, but it does not show that the same program works for every vehicle or on staircases. The study specifically discusses movement on flat ground and particular robot designs. The real robot was driven manually for three minutes first, so the researchers could learn how it moved.