2014/05/21 by Raphaël Lallement, Lallement, Raphaël, Lavindra de Silva +3 · 2 citations
Computer Science · Engineering · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Robotic Path Planning Algorithms #Robotics (cs.RO) #Robotics and Automated Systems #cs.AI #cs.RO
paper · pdf · doi:10.48550/arxiv.1405.5345
2nd ICAPS Workshop on Planning and Robotics, PlanRob 2014
openalex publication_date 2014/05/21 · arxiv created 2014/06/12 · arxiv updated 2014/06/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.