  Many works have recently explored Sim-to-real transferable visual model
predictive control (MPC). However, such works are limited to one-shot transfer,
where real-world data must be collected once to perform the sim-to-real
transfer, which remains a significant human effort in transferring the models
learned in simulations to new domains in the real world. To alleviate this
problem, we first propose a novel model-learning framework called Kalman
Randomized-to-Canonical Model (KRC-model). This framework is capable of
extracting task-relevant intrinsic features and their dynamics from randomized
images. We then propose Kalman Randomized-to-Canonical Model Predictive Control
(KRC-MPC) as a zero-shot sim-to-real transferable visual MPC using KRC-model.
The effectiveness of our method is evaluated through a valve rotation task by a
robot hand in both simulation and the real world, and a block mating task in
simulation. The experimental results show that KRC-MPC can be applied to
various real domains and tasks in a zero-shot manner.
