Section: Research Program

Task based world modeling and understanding

Executing a robotic task needs to specify a task space and a set of objective functions to be optimized. One research issue will be to define a framework allowing to represent the tasks in a generic canonic space in order to make their design and their analysis easier thanks to the control theory tools (observability, controllability, robustness…). All along the execution of the task, autonomous robotics systems have to acquire and maintain a model of the world and of the interactions between the different components involved in the task (heterogeneous robots, human beings, changes in the environment…). This model evolves in time and in space. In this research axis, we will investigate novel task-oriented world multi-layers representations (photometry, geometry, semantic) embedded in a short/long term memory framework able to handle static and dynamic events (long term mapping). A particular attention will be also paid to integrate human-robot interactions in shared environment (social skills). Another ambition of the project will be to build a bridge between model-based and machine learning methods. Understanding the world evolution is one of the key of autonomy. In this aim, we will focus on situation awareness.