HATP: An HTN Planner for Robotics
This work addresses the need for more intuitive and flexible planning tools for roboticists, though it appears incremental as it builds on existing HTN methods with domain-specific enhancements.
The authors tackled the problem of making Hierarchical Task Network (HTN) planning more suitable for robotics by extending its representation and semantics, resulting in the HATP framework that integrates social rules and geometric reasoning for real-time validation of actions in 3D environments.
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.