生物力学比较揭示人类与仿人步态的分歧
Biomechanical Comparisons Reveal Divergence of Human and Humanoid Gaits
AI总结：
本研究提出GDAF框架，通过比较人类与仿人步态的生物力学差异，揭示现代仿人控制器在步态对称性、能量分布和关节协调方面的系统性偏差，为提升仿人运动的生物力学保真度和节能性提供基准和工具。
摘要：
实现腿部机器人的人类样运动仍然具有挑战性，因为生物结构和机械结构之间存在根本性差异。尽管模仿学习已出现作为生成自然机器人运动的有前途的方法，但仅仅复制关节角度轨迹无法捕捉人类运动的原理。本研究提出了一种步态分歧分析框架（GDAF），即一个统一的生物力学评估框架，系统地量化人类和双足机器人之间的运动学和动力学差异。我们应用GDAF，系统地比较人类和仿人运动在28种行走速度下的情况。为了实现可重复分析，我们收集并发布了从最先进的仿人控制器中获得的连续速度仿人运动数据集。我们进一步提供了GDAF的开源实现，包括分析、可视化和基于MuJoCo的工具，使仿人运动的生物力学分析能够实现定量、可解释和可重复。结果表明，尽管现代仿人控制器生成的运动在视觉上类似于人类，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方面表现出系统性偏差，表明在提高仿人运动的生物力学保真度和能量效率方面仍有很大改进空间。本工作为评估仿人运动提供了定量基准，并提供了数据和多功能工具，以支持开发更接近人类和更节能的运动控制器。数据和代码将在论文接受后公开发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
