WestWorld: A Knowledge-Encoded Scalable

Our WestWorld outperforms the strongest baseline TrajWorld in real-world deployment on the Go1 quadruped. Our method successfully completes the straight-walking task and reaches the goal, whereas TrajWorld fails to reliably stand up and move toward the target. Because MPPI relies on accurate long-horizon rollouts, TrajWorld's inaccurate predictions lead to poor action selection and control failure. In contrast, our method remains accurate enough to support stable real-world control, despite being trained only on an offline simulation dataset.