Acting and Planning with Hierarchical Operational Models on a Mobile Robot: A Study with RAE+UPOM

TL;DR

This paper presents the first real-world deployment of an integrated hierarchical actor-planner system on a mobile robot, combining RAE and UPOM to improve robustness in task execution amidst uncertainties.

Contribution

It introduces a novel integrated actor-planner system sharing hierarchical models, combining reactive acting with Monte Carlo planning for real-world robotic tasks.

Findings

Robust task execution despite action failures and sensor noise

Empirical insights into interleaved acting and planning processes

Successful deployment on a mobile manipulator in real-world scenarios

Abstract

Robotic task execution faces challenges due to the inconsistency between symbolic planner models and the rich control structures actually running on the robot. In this paper, we present the first physical deployment of an integrated actor-planner system that shares hierarchical operational models for both acting and planning, interleaving the Reactive Acting Engine (RAE) with an anytime UCT-like Monte Carlo planner (UPOM). We implement RAE+UPOM on a mobile manipulator in a real-world deployment for an object collection task. Our experiments demonstrate robust task execution under action failures and sensor noise, and provide empirical insights into the interleaved acting-and-planning decision making process.