  As robotic systems move from highly structured environments to open worlds,
incorporating uncertainty from dynamics learning or state estimation into the
control pipeline is essential for robust performance. In this paper we present
a nonlinear particle model predictive control (PMPC) approach to control under
uncertainty, which directly incorporates any particle-based uncertainty
representation, such as those common in robotics. Our approach builds on
scenario methods for MPC, but in contrast to existing approaches, which either
constrain all or only the first timestep to share actions across scenarios, we
investigate the impact of a \textit{partial consensus horizon}. Implementing
this optimization for nonlinear dynamics by leveraging sequential convex
optimization, our approach yields an efficient framework that can be tuned to
the particular information gain dynamics of a system to mitigate both
over-conservatism and over-optimism. We investigate our approach for two
robotic systems across three problem settings: time-varying, partially observed
dynamics; sensing uncertainty; and model-based reinforcement learning, and show
that our approach improves performance over baselines in all settings.
