PRIOR: Perceptive Learning for Humanoid Locomotion with Reference Gait Priors

2026-03-19

AI Summary

This work proposes an end-to-end perception-control framework for learning natural bipedal locomotion in complex terrains without requiring multi-stage training, adversarial objectives, or extensive real-world calibration. Built upon Isaac Lab, the approach integrates a motion-capture-driven parametric gait generator, a GRU-based self-supervised terrain estimator that relies solely on egocentric depth images, and a terrain-adaptive foot placement reward mechanism to jointly optimize perception and motor control. The method achieves 100% success rate in traversing challenging terrains—including stairs, steps, and gaps—while significantly reducing perceptual computational overhead and producing stable, human-like gaits. The complete framework is open-sourced to support reproducible research.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• humanoid locomotion
• perceptive walking
• natural gait
• complex terrain traversal
• reference gait priors

Innovation

• perceptive locomotion
• parametric gait generator
• self-supervised heightmap reconstruction
• terrain-adaptive footstep rewards
• humanoid navigation