  Humans naturally exploit haptic feedback during contact-rich tasks like
loading a dishwasher or stocking a bookshelf. Current robotic systems focus on
avoiding unexpected contact, often relying on strategically placed environment
sensors. Recently, contact-exploiting manipulation policies have been trained
in simulation and deployed on real robots. However, they require some form of
real-world adaptation to bridge the sim-to-real gap, which might not be
feasible in all scenarios. In this paper we train a contact-exploiting
manipulation policy in simulation for the contact-rich household task of
loading plates into a slotted holder, which transfers without any fine-tuning
to the real robot. We investigate various factors necessary for this zero-shot
transfer, like time delay modeling, memory representation, and domain
randomization. Our policy transfers with minimal sim-to-real gap and
significantly outperforms heuristic and learnt baselines. It also generalizes
to plates of different sizes and weights. Demonstration videos and code are
available at https://sites.google.com/view/compliant-object-insertion.
