  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.
