Terrain Traversal Without Motion Priors: Expert-Routed Humanoid Locomotion at Deployment Fidelity

arXiv:2610.14061v1 Announce Type: new Abstract: State-of-the-art perceptive humanoid locomotion is mostly built on retargeted motion capture, a generative motion prior, and a distillation stage. We report a complete system that does not require this pipeline for a humanoid to cross varied terrain. In its place, we provide a five-expert mixture routed by a terrain classifier over a sensed elevation grid, and target every foothold with a model-based planner, refined by six closed-form-gradient steps on an interpolated terrain-cost field instead of picked from a fixed grid. Driven through the same perception-to-actuation software a robot would run, inside a second, deployment-fidelity simulator, it crosses an 85,m five-segment waypoint course 18 of 20 times end-to-end (90%, Wilson 95% CI 70--97) and succeeds 20/20 on four of five segments scored alone, where an ablated configuration without the mixture and without refinement reaches 0% on both the stair and the hurdle segment. The continuous refinement moves 72.4% of foot placements across at least one grid-cell boundary, which produces real sub-grid correction. We also ran matched component studies to report what does not account for it: one expert equals five on the training distribution, four label-free routers collapse to near-uniform activation, and a self-supervised terrain embedding loses to the privileged label.

Source: arXiv cs.RO | 2026-10-14