PRIOR: Perceptive Learning for Humanoid Locomotion with Reference Gait Priors

TL;DR

PRIOR is a framework enabling humanoid robots to traverse complex terrains using human-like gaits, combining a gait generator, terrain-aware perception, and adaptive footstep rewards for robust, real-time locomotion.

Contribution

It introduces a simple, reproducible approach that avoids adversarial training, integrating a parametric gait generator, self-supervised terrain perception, and terrain-adaptive rewards.

Findings

Achieves 100% traversal success across various terrains.

Reduces perceptual processing overhead without sacrificing performance.

Demonstrates effectiveness of each component through systematic experiments.

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.