Robots must make and break contact with the environment to perform useful tasks, but planning and control through contact remains a formidable challenge. In this work, we achieve real-time contact-implicit model predictive control with a surprisingly simple method: inverse dynamics trajectory optimization. While trajectory optimization with inverse dynamics is not new, we introduce a series of incremental innovations that collectively enable fast model predictive control on a variety of challenging manipulation and locomotion tasks. We implement these innovations in an open-source solver and present simulation examples to support the effectiveness of the proposed approach. Additionally, we demonstrate contact-implicit model predictive control on hardware at over 100 Hz for a 20-degree-of-freedom bi-manual manipulation task. Video and code are available atthis https URL.
View on arXiv@article{kurtz2025_2309.01813,
title={ Inverse Dynamics Trajectory Optimization for Contact-Implicit Model Predictive Control },
author={ Vince Kurtz and Alejandro Castro and Aykut Özgün Önol and Hai Lin },
journal={arXiv preprint arXiv:2309.01813},
year={ 2025 }
}