  In manipulation tasks like plug insertion or assembly that have low tolerance
to errors in pose estimation (errors of the order of 2mm can cause task
failure), the utilization of touch/contact modality can aid in accurately
localizing the object of interest. Motivated by this, in this work we model
high-precision insertion tasks as planning problems under pose uncertainty,
where we effectively utilize the occurrence of contacts (or the lack thereof)
as observations to reduce uncertainty and reliably complete the task. We
present a preprocessing-based planning framework for high-precision insertion
in repetitive and time-critical settings, where the set of initial pose
distributions (identified by a perception system) is finite. The finite set
allows us to enumerate the possible planning problems that can be encountered
online and preprocess a database of policies. Due to the computational
complexity of constructing this database, we propose a general experience-based
POMDP solver, E-RTDP-Bel, that uses the solutions of similar planning problems
as experience to speed up planning queries and use it to efficiently construct
the database. We show that the developed algorithm speeds up database creation
by over a factor of 100, making the process computationally tractable. We
demonstrate the effectiveness of the proposed framework in a real-world plug
insertion task in the presence of port position uncertainty and a pipe assembly
task in simulation in the presence of pipe pose uncertainty.
