Post-doctoral position (1 year): Perception for interaction and social navigation

Laboratory: LITIS, INSA Rouen Normandy, France

Project: INCA (Natural Interactions with Artificial Companions)

Summary:

The emergence of interactive robots and connected objects has lead to the appearance of symbiotic systems made up of human users, virtual agents and robots in social interactions. However, two major scientific difficulties are unsolved yet: on the one hand, the recognition of human activity remains inaccurate, both at the operational level (location, mapping and identification of objects and users) and cognitive (recognition and tracking of users’ intentions) and, on the other hand, interaction involves different modalities that must be adapted according to the context, the user and the situation. The INCA project aims at developing artificial companions (interactive robots and virtual agents) with a particular focus on social interactions. Our goal is to develop new models and algorithms for intelligent companions capable of (1) perceiving and representing an environment (real, virtual or mixed) consisting of objects, robots and users; (2) interacting with users in a natural way to assess their needs, preferences, and engagement; (3) learning models of user behavior and (4) generating semantically adequate and socially appropriate responses.

1 year of Post-doctoral position in perception for interaction and social navigation

The candidate will work to ensure that a robot can recognize the physical content of the scene surrounding him, recognize himself, static and dynamic objects (users and other robots) and finally predict the movement of dynamic elements. The integration of data from different sensors should allow the mapping of an unknown environment and estimate the position of the robot. First, VSLAM techniques (Visual Simultaneous Localization And Mapping) (Saputra 2018) will be used to map the scene. The regions (or points) of interest detected could
then be used to detect obstacles. In order to distinguish between static and dynamic objects, methods of separating the background from the foregound of the scene (Kajo et al, 2018) will be used. Finally, some recent techniques of the Flownet 2.0 type (Eddy et al, 2017), for the prediction of the motion on a video sequence should make it possible to predict the next movement of an object dynamic object and the to apprehend its behavior.

Profile: the candidate must have strong skills in mobile robotics and navigation techniques (VSLAM, OrbSlam, Optical Flow, stereovision…) and a high programming capacities under ROS or any other programming language compatible with robotics. Machine learning and Deep learning skills will be highly appreciated.

Duration and remuneration: 1 year, 2480euros/month (gross salary)

Application should be sent to: alexandre.pauchet@insa-rouen.fr samia.ainouz@insa-rouen.fr

• Curriculum vitae

• Cover letter

• Recommendation letters