activity
20162021
most citedEnd-User Programming of Low- and High-Level Actions for Robotic Task Planning

14 citations · 24 across the 5 of their papers we have counts for

collaborators

5 papers

cs.RO202114 cited

End-User Programming of Low- and High-Level Actions for Robotic Task Planning

Ying Siu Liang, Damien Pellier, Humbert Fiorino +1

Programming robots for general purpose applications is extremely challenging due to the great diversity of end-user tasks ranging from manufacturing environments to personal homes.…

cs.AI20212 cited

From Classical to Hierarchical: benchmarks for the HTN Track of the International Planning Competition

Damien Pellier, Humbert Fiorino

In this short paper, we outline nine classical benchmarks submitted to the first hierarchical planning track of the International Planning competition in 2020. All of these benchma…

cs.AI20206 cited

Totally and Partially Ordered Hierarchical Planners in PDDL4J Library

Damien Pellier, Humbert Fiorino

In this paper, we outline the implementation of the TFD (Totally Ordered Fast Downward) and the PFD (Partially ordered Fast Downward) hierarchical planners that participated in the…

cs.AI20202 cited

AMLSI: A Novel Accurate Action Model Learning Algorithm

Maxence Grand, Humbert Fiorino, Damien Pellier

This paper presents new approach based on grammar induction called AMLSI Action Model Learning with State machine Interactions. The AMLSI approach does not require a training datas…

cs.AI2016

Learning Macro-actions for State-Space Planning

Sandra Castellanos-Paez, Damien Pellier, Humbert Fiorino +1

Planning has achieved significant progress in recent years. Among the various approaches to scale up plan synthesis, the use of macro-actions has been widely explored. As a first s…