  Sample-based learning model predictive control (LMPC) strategies have
recently attracted attention due to their desirable theoretical properties and
their good empirical performance on robotic tasks. However, prior analysis of
LMPC controllers for stochastic systems has mainly focused on linear systems in
the iterative learning control setting. We present a novel LMPC algorithm,
Adjustable Boundary Condition LMPC (ABC-LMPC), which enables rapid adaptation
to novel start and goal configurations and theoretically show that the
resulting controller guarantees iterative improvement in expectation for
stochastic nonlinear systems. We present results with a practical instantiation
of this algorithm and experimentally demonstrate that the resulting controller
adapts to a variety of initial and terminal conditions on 3 stochastic
continuous control tasks.
