Reactive Informative Planning for Mobile Manipulation Tasks under Sensing and Environmental Uncertainty

TL;DR

This paper presents a hybrid control architecture for mobile manipulation under sensing and environmental uncertainty, enabling robots to actively reduce uncertainty and adapt to unknown environments during task execution.

Contribution

It introduces a novel integrated framework combining symbolic, informative, and reactive control for robust manipulation in uncertain settings.

Findings

Successfully handles complex tasks with environmental uncertainty.

Adapts online to unanticipated adverse configurations.

Improves scalability over existing methods.

Abstract

In this paper we address mobile manipulation planning problems in the presence of sensing and environmental uncertainty. In particular, we consider mobile sensing manipulators operating in environments with unknown geometry and uncertain movable objects, while being responsible for accomplishing tasks requiring grasping and releasing objects in a logical fashion. Existing algorithms either do not scale well or neglect sensing and/or environmental uncertainty. To face these challenges, we propose a hybrid control architecture, where a symbolic controller generates high-level manipulation commands (e.g., grasp an object) based on environmental feedback, an informative planner designs paths to actively decrease the uncertainty of objects of interest, and a continuous reactive controller tracks the sparse waypoints comprising the informative paths while avoiding a priori unknown obstacles. The overall architecture can handle environmental and sensing uncertainty online, as the robot explores its workspace. Using numerical simulations, we show that the proposed architecture can handle tasks of increased complexity while responding to unanticipated adverse configurations.