Arthur Bit-Monnot

About

Arthur Bit-Monnot is a leading researcher in AI planning and robotics, whose work bridges the gap between high-level deliberation and real-world robotic execution. His core research areas include temporal and hierarchical planning, human-robot interaction, and situated reasoning. He is best known for developing FAPE (Flexible Acting and Planning Environment), a groundbreaking framework that integrates acting and planning using the ANML modeling language—a key contribution that combines the expressiveness of timeline representations with hierarchical decomposition methods. This work, detailed in his most-cited paper (34 citations), has been foundational for enabling robots to handle complex, time-sensitive tasks. Bit-Monnot’s impact extends to human-robot collaboration, where he has pioneered methods for efficient, ontology-based referring expression generation, ensuring unambiguous communication between humans and robots. His research on SMT-based planning for smart factories (6 citations) further demonstrates his ability to apply theoretical advances to industrial settings. With a publication record spanning over a decade, Bit-Monnot’s work has shaped how robots deliberate and act in dynamic environments, making him a pivotal figure in the integration of AI planning with autonomous systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Planning and Acting with Temporal and Hierarchical Decomposition Models

   34 citations · 2014
2. 2
   A Flexible ANML Actor and Planner in Robotics

   19 citations · 2014
3. 3
   Temporal and Hierarchical Models for Planning and Acting in Robotics

   11 citations · 2016
4. 4
   Efficient, Situated and Ontology based Referring Expression Generation for Human-Robot collaboration

   9 citations · 2020
5. 5
   SMT-based Planning for Robots in Smart Factories

   6 citations · 2019

Key Collaborators

Contact & Links