It remains challenging to achieve human-like locomotion in legged robots due to fundamental discrepancies between biological and mechanical structures. Although imitation learning has emerged as a promising approach for generating natural robotic movements, simply replicating joint angle trajectories fails to capture the underlying principles of human motion. This study proposes a Gait Divergence Analysis Framework (GDAF), a unified biomechanical evaluation framework that systematically quantifies kinematic and kinetic discrepancies between humans and bipedal robots. We apply GDAF to systematically compare human and humanoid locomotion across 28 walking speeds. To enable reproducible analysis, we collect and release a speed-continuous humanoid locomotion dataset from a state-of-the-art humanoid controller. We further provide an open-source implementation of GDAF, including analysis, visualization, and MuJoCo-based tools, enabling quantitative, interpretable, and reproducible biomechanical analysis of humanoid locomotion. Results demonstrate that despite visually human-like motion generated by modern humanoid controllers, significant biomechanical divergence persists across speeds. Robots exhibit systematic deviations in gait symmetry, energy distribution, and joint coordination, indicating that substantial room remains for improving the biomechanical fidelity and energetic efficiency of humanoid locomotion. This work provides a quantitative benchmark for evaluating humanoid locomotion and offers data and versatile tools to support the development of more human-like and energetically efficient locomotion controllers. The data and code will be made publicly available upon acceptance of the paper.

翻译：由于生物结构与机械结构之间存在根本性差异，在足式机器人中实现类人运动仍然具有挑战性。尽管模仿学习已成为生成自然机器人运动的一种有前景的方法，但单纯复制关节角度轨迹无法捕捉人类运动的底层原理。本研究提出了步态差异分析框架（GDAF），这是一个统一的生物力学评估框架，可系统量化人类与双足机器人之间的运动学和动力学差异。我们应用GDAF系统比较了人类与人形机器人在28种步行速度下的运动。为实现可重复分析，我们从最先进的人形机器人控制器收集并发布了一个速度连续的人形机器人运动数据集。我们进一步提供了GDAF的开源实现，包括分析、可视化及基于MuJoCo的工具，支持对人形机器人运动进行定量、可解释且可重复的生物力学分析。结果表明，尽管现代人形机器人控制器能生成视觉上类人的运动，但在不同速度下仍存在显著的生物力学差异。机器人在步态对称性、能量分布和关节协调性方面表现出系统性偏差，这表明在提升人形机器人运动的生物力学保真度和能量效率方面仍有巨大改进空间。本研究为评估人形机器人运动提供了定量基准，并提供了数据和多功能工具以支持开发更具类人特性和能量效率的运动控制器。论文录用后，数据和代码将公开提供。