Duty Factor Predicts Robust Constrained Quadrupedal Locomotion Across Gait Types

2026-09-18

AI Summary

研究通过三种控制方法探讨了步态参数对四足机器人在受限地形中稳健行走的影响，发现占空比是预测和调整行走稳健性的有效参数。

Abstract

Quadrupedal robots are increasingly deployed in environments where locomotion must remain robust to disturbances and constrained terrain. Gait type, such as walking or trotting, is commonly used to characterize quadrupedal locomotion. However, gait type does not uniquely define locomotion, as parameters such as duty factor, speed, and stance width can vary within a single gait type. In this work, we investigate the relationship between these gait parameters using three distinct quadrupedal locomotion control approaches. First, using whole body trajectory optimization with LQR feedback, we show that duty factor is a stronger predictor of local error convergence than nominal gait type. Second, we investigate duty factor selection with a learned locomotion controller, suggesting how duty factor may serve as a low-dimensional parameter for adapting locomotion robustness in narrow-terrain environments. Finally, we show that these trends persist under a centroidal model predictive control framework and validate them through narrow-terrain experiments on a physical quadruped. These results show that duty factor provides a simple and effective basis for understanding and selecting robust quadrupedal locomotion across gait types and control architectures.

Problem

• Quadrupedal Locomotion
• Duty Factor
• Gait Type
• Robustness

Innovation

• Duty Factor
• Quadrupedal Locomotion
• Robustness
• Control Architecture