Knowledge-Based Hierarchical POMDPs for Task Planning

TL;DR

This paper introduces a hierarchical POMDP-based task planning architecture for service robots that encodes knowledge for modularity and reduces planning time while maintaining robustness in uncertain environments.

Contribution

It presents a recursive hierarchical POMDP framework that enables autonomous hierarchy building and improves planning efficiency in robotic task planning.

Findings

Significant reduction in planning time compared to baseline methods.

Maintains or improves robustness under various uncertainty scenarios.

Supports modular knowledge encoding for robot and environment.

Abstract

The main goal in task planning is to build a sequence of actions that takes an agent from an initial state to a goal state. In robotics, this is particularly difficult because actions usually have several possible results, and sensors are prone to produce measurements with error. Partially observable Markov decision processes (POMDPs) are commonly employed, thanks to their capacity to model the uncertainty of actions that modify and monitor the state of a system. However, since solving a POMDP is computationally expensive, their usage becomes prohibitive for most robotic applications. In this paper, we propose a task planning architecture for service robotics. In the context of service robot design, we present a scheme to encode knowledge about the robot and its environment, that promotes the modularity and reuse of information. Also, we introduce a new recursive definition of a POMDP that enables our architecture to autonomously build a hierarchy of POMDPs, so that it can be used to generate and execute plans that solve the task at hand. Experimental results show that, in comparison to baseline methods, by following a recursive hierarchical approach the architecture is able to significantly reduce the planning time, while maintaining (or even improving) the robustness under several scenarios that vary in uncertainty and size.