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Inverse dynamics trajectory optimization for contact-implicit model predictive control
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AI
DOI：10.1177/02783649251344635.png)
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Abstract
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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 at
https://idto.github.io
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Keywords:
contact-implicit control
model predictive control
inverse dynamics
trajectory optimization
real-time control
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