PRIOR: Perceptive Learning for Humanoid Locomotion with Reference Gait Priors
Training perceptive humanoid locomotion policies that traverse complex terrains with natural gaits remains an open challenge, typically demanding multi-stage training pipelines, adversarial objectives, or extensive real-world calibration. We present PRIOR, an efficient and reproducible framework built on Isaac Lab that achieves robust terrain traversal with human-like gaits through a simple yet effective design: (i) a parametric gait generator that supplies stable reference trajectories derived from motion capture without adversarial training, (ii) a GRU-based state estimator that infers terrain geometry directly from egocentric depth images via self-supervised heightmap reconstruction, a...
| Authors | Chenxi Han, Shilu He, Yi Cheng et al. |
|---|---|
| Primary category | cs.RO |
| Published | 19 March 2026 |
| Last updated at source | 19 March 2026 |
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