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Biomechanical Comparisons Reveal Divergence of Human and Humanoid Gaits

Luying Feng, Yaochu Jin, Hanze Hu, Wei Chen

Abstract

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.

Biomechanical Comparisons Reveal Divergence of Human and Humanoid Gaits

Abstract

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.
Paper Structure (29 sections, 12 equations, 8 figures, 1 table)

This paper contains 29 sections, 12 equations, 8 figures, 1 table.

Table of Contents

  1. INTRODUCTION
  2. Methodology
  3. Data Acquisition and Processing
  4. Human Gait Data Acquisition and Processing
  5. Robotic Gait Data Acquisition and Processing
  6. Joint Mapping
  7. A Gait Divergence Analysis Framework
  8. Data Preparation and Visualization
  9. Multi-dimensional Quantitative Divergence Analysis
  10. Comprehensive GDAF Cost
  11. Results
  12. Data Visualization
  13. Gait Divergence Analysis
  14. Waveform Similarity
  15. Bilateral Symmetry
  16. ...and 14 more sections

Figures (8)

  • Figure 1: Joint mapping between humanoid robot and human.
  • Figure 2: Comparison of lower-limb joint angles, moments, and power between human subjects and humanoid robot at three walking speeds.
  • Figure 3: Comparison of torque-angle loops between human subjects and humanoid robot at three walking speeds.
  • Figure 4: Multidimensional similarity heatmaps of joint angles, moments, and power across walking speeds
  • Figure 5: Bilateral symmetry index (SI) across joint pairs for human and robot.
  • ...and 3 more figures