  Simulation-to-reality transfer has emerged as a popular and highly successful
method to train robotic control policies for a wide variety of tasks. However,
it is often challenging to determine when policies trained in simulation are
ready to be transferred to the physical world. Deploying policies that have
been trained with very little simulation data can result in unreliable and
dangerous behaviors on physical hardware. On the other hand, excessive training
in simulation can cause policies to overfit to the visual appearance and
dynamics of the simulator. In this work, we study strategies to automatically
determine when policies trained in simulation can be reliably transferred to a
physical robot. We specifically study these ideas in the context of robotic
fabric manipulation, in which successful sim2real transfer is especially
challenging due to the difficulties of precisely modeling the dynamics and
visual appearance of fabric. Results in a fabric smoothing task suggest that
our switching criteria correlate well with performance in real. In particular,
our confidence-based switching criteria achieve average final fabric coverage
of 87.2-93.7% within 55-60% of the total training budget. See
https://tinyurl.com/lsc-case for code and supplemental materials.
