Layered Control of Partially Observed Stochastic Systems

TL;DR

This paper develops a layered control framework for partially observed stochastic systems, introducing stochastic simulation functions and demonstrating their application on aerial robotic systems.

Contribution

It provides a novel theoretical foundation for layered control under partial observations and stochastic noise, including systematic construction methods for linear systems with Kalman estimators.

Findings

Framework ensures expected output distance bounds between layers.

Constructs stochastic simulation functions for linear systems with Kalman estimators.

Validated on aerial robotic scenarios with UAVs and hexacopters.

Abstract

Layered control is essential for managing complexity in large-scale systems, employing progressively coarser models at higher layers. While significant advances have been made for fully observable systems, the theoretical foundations of layered control under partial observations and stochastic noise remain underexplored. To address this gap, we propose a principled layered control framework for such settings. Given a state estimator at each layer, our approach ensures that the expected output distance between systems at successive layers remains within a priori computable bounds. This is achieved by introducing a novel notion of stochastic simulation functions for partially observed systems. For the class of linear systems with Kalman estimators, we provide a systematic construction of these functions along with the corresponding control design. We demonstrate our framework on two aerial robotic scenarios: an unmanned aerial vehicle and a hexacopter with a camera payload.