PRIOR: Perceptive Learning for Humanoid Locomotion with Reference Gait Priors

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.

Figures (6)

• Figure 1: The proposed PRIOR framework was simulated and demonstrated on the ZERITH Z1 model. (A)--(D) illustrate the robot traversing four representative terrain types.
• Figure 2: Overview of the proposed PRIOR framework. The framework comprises three components: (a) Asymmetric actor-critic architecture for reinforcement learning. (b) State Estimator (yellow): Fuses multimodal latent features for policy driving and performs self-supervised regression for velocity estimation, terrain reconstruction, and state prediction. (c) Reference Gait Generator (blue): Synthesizes physics-consistent reference trajectories via phase normalization and velocity-driven weighted interpolation, while constraining locomotion through gait-aware rewards.
• Figure 3: Depth data flow: Images in the top and bottom rows represent samples from different parallel environments.
• Figure 4: Overview of the terrain curriculum for training. (A)--(D) represent distinct terrain types posing various physical challenges. Table (E) details the parameter ranges and the distribution (weight) for each terrain type.
• Figure 5: The upper panel illustrates the training curves for mean reward, while the lower panel displays the mean terrain level achieved over training iterations.