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Machine Learning-Robotics

Domaine
Machine Learning-Robotics
Domain - extra
Année
2014
Starting
Sept. 2014
État
Open
Sujet
Developpemental Reinforcement Learning
Thesis advisor
SEBAG Michèle
Co-advisors
Marc Schoenauer, INRIA
Laboratory
Collaborations
U. Delft, Germany
Abstract
Reinforcement learning "in-situ" avoids the so-called reality gap. In the meanwhile, the trained agent incurs hazards (mechanical fatigue or accidents).
The proposed developmental RL approach is remotely inspired by the learning of motion primitives by infants. Natural limitations prevent infants from endangering their body when exploring their sensori-motor abilities. As children grow, their musculoskeletal system evolves and becomes stronger. Adults, who can operate with full strength, are able to seriously injure themselves; but they are assumedly skilled enough to avoid injuries with high probability.
The PhD defines a robotic developmental process by gradually extending the actuator ranges. External rewards are used to determine when and how the robot actuator range can be extended. Then, the intensity of the robot actions will increase as its skills improve.
The process will be iterated until the robot is able to deploy the desired skills with full strength.


Context
Objectives
Work program
Extra information
Prerequisite
Détails
Expected funding
Research contract
Status of funding
Expected
Candidates
Utilisateur
michele-martine.sebag
Créé
Jeudi 12 juin 2014 23:32:53 CEST
dernière modif.
Jeudi 12 juin 2014 23:32:53 CEST

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Ecole Doctorale Informatique Paris-Sud


Directrice
Nicole Bidoit
Assistante
Stéphanie Druetta
Conseiller aux thèses
Dominique Gouyou-Beauchamps

ED 427 - Université Paris-Sud
UFR Sciences Orsay
Bat 650 - aile nord - 417
Tel : 01 69 15 63 19
Fax : 01 69 15 63 87
courriel: ed-info à lri.fr