En poursuivant votre navigation sur ce site, vous acceptez le dépôt de cookies dans votre navigateur. (En savoir plus)
Portail > Offres > Offre UMR5217-DAMPEL-001 - Post-doctorant H/F IA/Robotique

PostDoc. Position : Artificial Intelligence/Robotics (M/F)

This offer is available in the following languages:
Français - Anglais

Assurez-vous que votre profil candidat soit correctement renseigné avant de postuler. Les informations de votre profil complètent celles associées à chaque candidature. Afin d’augmenter votre visibilité sur notre Portail Emploi et ainsi permettre aux recruteurs de consulter votre profil candidat, vous avez la possibilité de déposer votre CV dans notre CVThèque en un clic !

Faites connaître cette offre !

General information

Reference : UMR5217-DAMPEL-001
Workplace : GRENOBLE
Date of publication : Wednesday, November 21, 2018
Type of Contract : FTC Scientist
Contract Period : 12 months
Expected date of employment : 1 January 2019
Proportion of work : Full time
Remuneration : From 2530 to 2919
Desired level of education : PhD
Experience required : Indifferent

Missions

Since their emergence, industrial robots are traditionally programmed off-line by experts in robotics using computer-based coding interfaces. Robots are then placed in working plants to be used by human operators in order to execute repetitive tasks complying with code instructions. When the tasks assigned to the robot have to change, this robot needs to be completely recoded by the robotics experts, which is challenging, takes a lot of human efforts and time, and does not meet nowadays industry needs in supply-chain reconfigurations and flexibility.

Activities

To address this challenge, the post-doc recruited will have to devise a new human-friendly approach for industrial robot programming through on-site Human-Robot Interactions. The approach consists of three processes: human teaching, robot learning and robot execution: 1. The human operator that will use the robot teaches it a task by providing verbal instructions and by manipulating the robot's effectors. 2. The robot learning process is based on learning by demonstration techniques [1]. In this process, the human operator and the robot build a common symbolic representation of the task: this symbolic representation is a planning domain description language1. This language uses preconditions and effects to describe changes in the robot's context. 3. The robot task execution is controlled by human verbal instructions that express the objectives of the task that the robot has to achieve. Then, the robot automatically computes a sequence of gestures achieving these objectives: the human operator does not provide the “recipe”, i.e., a sequence of gestures to achieve the objectives; the “recipe” is computed by Automated Planning techniques [2]. For this project, we will use the PDDL4J library (pddl4j.imag.fr). The development will be implemented on the Baxter robotic platforms

REFERENCES
[1] B. Argall, S. Chernova, M. Veloso, B. Browning. A survey of robot learning from demonstration. Robotics and Autonomous Systems. Volume 57, Issue 5, 31 May 2009, Pages 469–483.
[2] M. Ghallab, D. Nau and P. Traverso, “Automated Planning”, Morgan-Kaufman, 2004.

Skills

Technical skills: Java, Python, ROS

Work Context

Grenoble Informatics Laboratory (LIG) is one of the largest laboratories in Computer Science in France. 500 members of LIG (faculty, full-time researchers, PhD students, administrative and technical staff) are distributed over three sites in Grenoble and its suburbs: the Saint Martin d'Hères Campus, Minatec, and the Montbonnot Campus.

We talk about it on Twitter!