  We present a two-stage framework that integrates a learning-based estimator
and a controller, designed to address contact-intensive tasks. The estimator
leverages a Bayesian particle filter with a mixture density network (MDN)
structure, effectively handling multi-modal issues arising from contact
information. The controller combines a self-supervised and reinforcement
learning (RL) approach, strategically dividing the low-level admittance
controller's parameters into labelable and non-labelable categories, which are
then trained accordingly. To further enhance accuracy and generalization
performance, a transformer model is incorporated into the self-supervised
learning component. The proposed framework is evaluated on the bolting task
using an accurate real-time simulator and successfully transferred to an
experimental environment. More visualization results are available on our
project website: https://sites.google.com/view/2stagecitt
