HATP: An HTN Planner for Robotics

TL;DR

HATP is a hierarchical task network planner tailored for robotics, integrating social rules and geometric reasoning to generate feasible robot and agent behaviors in complex 3D environments.

Contribution

The paper introduces HATP, a novel HTN planning framework that incorporates social rules and geometric validation, enhancing planning for robotic applications.

Findings

Successfully integrates social rules into HTN planning.

Enables online geometric validation of actions.

Improves planning efficiency in complex 3D environments.

Abstract

Hierarchical Task Network (HTN) planning is a popular approach that cuts down on the classical planning search space by relying on a given hierarchical library of domain control knowledge. This provides an intuitive methodology for specifying high-level instructions on how robots and agents should perform tasks, while also giving the planner enough flexibility to choose the lower-level steps and their ordering. In this paper we present the HATP (Hierarchical Agent-based Task Planner) planning framework which extends the traditional HTN planning domain representation and semantics by making them more suitable for roboticists, and treating agents as "first class" entities in the language. The former is achieved by allowing "social rules" to be defined which specify what behaviour is acceptable/unacceptable by the agents/robots in the domain, and interleaving planning with geometric reasoning in order to validate online -with respect to a detailed geometric 3D world- the human/robot actions currently being pursued by HATP.