  We propose a method for planning motion for robots with actuation uncertainty
that incorporates contact with the environment and the compliance of the robot
to reliably perform manipulation tasks. Our approach consists of two stages:
(1) Generating partial policies using a sampling-based motion planner that uses
particle-based models of uncertainty and simulation of contact and compliance;
and (2) Resilient execution that updates the planned policies to account for
unexpected behavior in execution which may arise from model or environment
inaccuracy. We have tested our planner and policy execution in simulated SE(2)
and SE(3) environments and Baxter robot. We show that our methods efficiently
generate policies to perform manipulation tasks involving significant contact
and compare against several simpler methods. Additionally, we show that our
policy adaptation is resilient to significant changes during execution; e.g.
adding a new obstacle to the environment.
