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