# Gait-Conditioned Reinforcement Learning for Humanoid Locomotion
**Source:** https://arxiv.org/abs/2505.20619
**Fetched:** 2026-02-13
**Type:** Research Paper

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## Paper Information

- **arXiv ID:** 2505.20619
- **Authors:** Tianhu Peng, Lingfan Bao, Chengxu Zhou
- **Initial Submission:** May 27, 2025
- **Latest Revision:** September 15, 2025 (version 3)

## Abstract

This paper presents a unified reinforcement learning framework enabling humanoid robots to execute multiple locomotion modes within a single policy.

## Core Technical Contributions

### Gait-Conditioned Architecture

The system uses a compact reward routing mechanism that employs one-hot gait identifiers to activate mode-specific objectives, reducing reward conflicts across different movement types.

### Multi-Phase Curriculum

A structured progression introduces movement complexity systematically while expanding the command space across multiple training phases.

### Biomechanically-Inspired Rewards

The framework incorporates human-inspired reward components promoting natural motions like straight-knee standing and coordinated arm-leg movement, without requiring motion capture datasets.

## Experimental Validation

### Simulation Results

The policy successfully achieved standing, walking, running, and smooth transitions between gaits.

### Real-World Testing on Unitree G1

On the Unitree G1 humanoid robot, researchers validated standing, walking, and walk-to-stand transitions, demonstrating "stable and coordinated locomotion."

## Significance

This work offers "a scalable, reference-free solution toward versatile and naturalistic humanoid control across diverse modes and environments," representing progress in multi-modal robot locomotion without motion capture dependencies.