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The Angular-Momentum Linear Inverted Pendulum (ALIP) model is a promising motion planner for bipedal robots. However, it relies on two assumptions: (1) the robot has point-contact feet or passive ankles, and (2) the angular momentum around the center of mass, known as centroidal angular momentum, is negligible. This paper addresses the question of whether the ALIP paradigm can be applied to more general bipedal systems with complex foot geometry (e.g., flat feet) and nontrivial torso/limb inertia and mass distribution (e.g., non-centralized arms). In such systems, the dynamics introduce non-negligible centroidal momentum and contact wrenches at the feet, rendering the assumptions of the ALIP model invalid. This paper presents the ALIP planner for general bipedal robots with non-point-contact feet through the use of a task-space whole-body controller that regulates centroidal momentum, thereby ensuring that the robot's behavior aligns with the desired template dynamics. To demonstrate the effectiveness of our proposed approach, we conduct simulations using the Sarcos©Guardian® XO®robot, which is a hybrid humanoid/exoskeleton with large, offset feet. The results demonstrate the practicality and effectiveness of our approach in achieving stable and versatile bipedal locomotion.
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This content will become publicly available on July 8, 2026
Koopman Modeling of Human Gait Dynamics for Global Modal Analysis Using Periodic Motion Regularization
This paper presents a data driven global linear model of steady state walking dynamics. Instantaneous whole body angular momentum is a physics informed aggregate quantity used as a marker for dynamic balance during locomotion. Gait dynamics are often modeled as hybrid and nonlinear. We propose using Koopman Operators to model the gait stability dynamics with a global, linear model. This is achieved by augmenting the whole body angular momentum state variables with learned observables, or basis functions, such that the dynamics look linear in the lifted dimension. Considering that the gait dynamics are periodic, a regularization term that encourages the state transition matrix to be orthonormal is added to the loss term when learning the observables. This forces a periodic model to be learned and prevents the likelihood of unstable poles. A low average MSE was obtained over 2 gait cycles for different population types, each with slightly differing gait dynamics. Furthermore, this linear representation enables the use of linear analysis tools that could have clinical implications for assessing the gait of different patient groups.
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- Award ID(s):
- 2133072
- PAR ID:
- 10613388
- Publisher / Repository:
- American Automatic Control Council
- Date Published:
- Subject(s) / Keyword(s):
- Koopman operators Gait dynamics Whole-body angular momentum
- Format(s):
- Medium: X
- Location:
- American Control Conference, Denver CO
- Sponsoring Org:
- National Science Foundation
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