Partially Observable Markov Decision Process for Managing Robot Collaboration with Human

Abir-Beatrice Karami , Laurent Jeanpierre (1) , Abdel-Illah Mouaddib (1)

1 Equipe MAD - Laboratoire GREYC - UMR6072

We present a new framework for controlling a robot collaborating with a human to accomplish a common mission. Knowing that we are interested in collaboration domains where there is no shared plan between the human and the robot, the constraints on the decision process are more challenging. We study the decision process of a robot agent for a specific shared mission with a human considering the effect of the human presence, the planning flexibility according to human comfortability and achieving mission. We choose to formalize this problem with Partially Observable Markov Decision Process, then we describe a new domain example that represent human-robot collaboration with no shared plan and we show some preliminary results of solving the POMDP model with standard optimal algorithms as a base work to compare with state-of-the-art and future-work approximate algorithms.

Domaines: Intelligence artificielle [cs.AI]; Modélisation et simulation; Système multi-agents [cs.MA]

Abir-Beatrice Karami, Laurent Jeanpierre, Abdel-Illah Mouaddib. Partially Observable Markov Decision Process for Managing Robot Collaboration with Human. 21st International Conference on Tools with Artificial Intelligence (ICTAI~2009), 2009, New Jersey, United States. ⟨hal-00969166⟩