Robots must make and break contact to interact with the world and perform
useful tasks. However, 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 a variety of 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