# HATP: An HTN Planner for Robotics

arXiv: https://arxiv.org/abs/1405.5345 · HTML: https://mlsanity.com/paper/1405.5345/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2014.

## Built on by (most cited first, 4 of 4)

- [HiRAS: A Hierarchical Multi-Agent Framework for Paper-to-Code Generation and Execution](https://mlsanity.com/paper/2604.17745/) (2026)
- [Unified Task and Motion Planning using Object-centric Abstractions of Motion Constraints](https://mlsanity.com/paper/2312.17605/) (2023)
- [Robust Planning for Human-Robot Joint Tasks with Explicit Reasoning on Human Mental State](https://mlsanity.com/paper/2210.08879/) (2022)
- [An Epistemic Human-Aware Task Planner which Anticipates Human Beliefs and Decisions](https://mlsanity.com/paper/2409.18545/) (2024)

## Abstract (arXiv)

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.