  We present FORGE, a method that enables sim-to-real transfer of contact-rich
manipulation policies in the presence of significant pose uncertainty. FORGE
combines a force threshold mechanism with a dynamics randomization scheme
during policy learning in simulation, to enable the robust transfer of the
learned policies to the real robot. At deployment, FORGE policies, conditioned
on a maximum allowable force, adaptively perform contact-rich tasks while
respecting the specified force threshold, regardless of the controller gains.
Additionally, FORGE autonomously predicts a termination action once the task
has succeeded. We demonstrate that FORGE can be used to learn a variety of
robust contact-rich policies, enabling multi-stage assembly of a planetary gear
system, which requires success across three assembly tasks: nut-threading,
insertion, and gear meshing. Project website can be accessed at
https://noseworm.github.io/forge/.
