Section: Overall Objectives
Main challenge:The project-team e-Motion aims at developing models and algorithms allowing us to build “artificial systems” including advanced sensorimotors loops, and exhibiting sufficiently efficient and robust behaviors for being able to operate in open and dynamic environments (i.e. in partially known environments, where time and dynamics play a major role), while leading to various types of interaction with humans . This Challenge is part of a more general challenge that we call Robots in Human Environments . Recent technological progress on embedded computational power, on sensor technologies, and on miniaturised mechatronic systems, make the required technological breakthroughs potentially possible (including from the scalability point of view).
Approach and research themes:In order to try to reach the previous objective, we combine the respective advantages of computational geometry and of the theory of probabilities . We are also working in cooperation with neurophysiologists on sensorimotor systems, for trying to apply and experiment some biological models . This approach leads us to study, under these different points of view, three strongly correlated fundamental research themes:
Perception and multimodal modelling of space and motion . The basic idea consists in continuously building (using preliminary knowledge and current perceptive data) several types of models having complementary functional specialisations (as suggested by neurophysiologists). This leads us to address the following questions : how to model the various aspects of the real world ? how to consistently combine a priori knowledge and flows of perceptive data ? how to predict the motions and behaviors of the sensed object ?
Motion planning and autonomous navigation in the physical world . The main problem is to simultaneously take into account various constraints of the physical world such as non-collision, environment dynamicity, or reaction time, while mastering the related algorithmic complexity. Our approach for solving this problem consists in addressing two main questions : how to construct incrementally efficient and reliable space-time representations for both motion planning and navigation ? how to define an iterative motion planning paradigm taking into account kinematics, dynamics, time constraints, and safety issues ? How to integrate Human-Robot interactions into the decisional processes ?
Learning, decision, and probabilistic inference . The main problem to solve is to be able to correctly reason about prior and learned knowledge, while taking explicitely into account the related uncertainty. Our approach for addressing this problem is to use and develop our bayesian programming paradigm, while collaborating with neurophysiologists on some particular topics such as the modeling of human navigation mechanisms or of biological sensorimotors loops. The main questions we are addressing are the followings : how to model sensorimotor systems and related behaviors ? how to take safe navigation decisions under uncertainty ? What kind of models and computational tools are required for implementing the related bayesian inference paradigms ?