Robust Humanoid Locomotion via Sequential Stepping and Angular Momentum Optimization

Journal Article (2025)
Author(s)

Jiatao Ding (TU Delft - Learning & Autonomous Control)

Cosimo Della Lieu (Deutsches Zentrum für Luft- und Raumfahrt (DLR), TU Delft - Learning & Autonomous Control)

Tin Lun Lam (The Chinese University of Hong Kong, Shenzhen)

Jianxin Pang

Xiaohui Xiao (Wuhan University)

Nikos G. Tsagarakis (Istituto Italiano di Tecnologia)

Yanlong Huang (University of Leeds)

DOI related publication
https://doi.org/10.1109/TIE.2024.3433451
More Info
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Publication Year
2025
Language
English
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
Issue number
3
Volume number
72
Pages (from-to)
2779-2789
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Abstract

Stepping strategy, including step time and step location modulation, and hip strategy, i.e., upper-body movement, play crucial roles in achieving robust humanoid locomotion. However, exploiting these balance strategies in a unified and flexible manner has not been well addressed. In this article, we propose a sequential convex optimization approach. Based on the linear inverted pendulum model, we modulate step parameters, including step location and step time, using quadratically constrained quadratic programming in real time. Then, based on the nonlinear inverted pendulum plus flywheel model, we regulate angular momentum using the linear model predictive control. To accommodate for scenarios with height variation, we consider nonlinear 3-D locomotion dynamics explicitly. The proposed approach is validated via comparison studies and extensive experiments on the humanoid with planar and linear feet. The results demonstrate enhanced robustness against dynamic disturbances and adaptability to real-world scenarios. On average, the enhanced stepping strategy rejects 135% larger external forces than our previous article. Also, robust locomotion across height-varying stepping stones is realized, which is rarely reported for a humanoid robot with planar feet.

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