PRIOR: Perceptive Learning for Humanoid Locomotion with Reference Gait Priors

• URL: http://arxiv.org/abs/2603.18979v1
• Date: Thu, 19 Mar 2026 14:42:07 GMT
• Title: PRIOR: Perceptive Learning for Humanoid Locomotion with Reference Gait Priors
• Abstract summary: PRIOR is an efficient and reproducible framework built on Isaac Lab.<n>We present a framework that achieves robust terrain traversal with human-like gaits.<n>We will open-source the complete PRIOR framework, including the training pipeline, parametric gait generator, and evaluation benchmarks.
• Score: 12.48639232018629
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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, and (iii) terrain-adaptive footstep rewards that guide foot placement toward traversable regions. Through systematic analysis of depth image resolution trade-offs, we identify configurations that maximize terrain fidelity under real-time constraints, substantially reducing perceptual overhead without degrading traversal performance. Comprehensive experiments across terrains of varying difficulty-including stairs, boxes, and gaps-demonstrate that each component yields complementary and essential performance gains, with the full framework achieving a 100% traversal success rate. We will open-source the complete PRIOR framework, including the training pipeline, parametric gait generator, and evaluation benchmarks, to serve as a reproducible foundation for humanoid locomotion research on Isaac Lab.

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