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Domain Machine Learning-Robotics
Domain - extra
Year 2014
Starting Sept. 2014
Status Open
Subject Developpemental Reinforcement Learning
Thesis advisor SEBAG Michèle
Co-advisors Marc Schoenauer, INRIA
Laboratory LRI A&O
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.

Work program
Extra information
Expected funding Research contract
Status of funding Expected
user michele-martine.sebag
Created Thursday 12 of June, 2014 23:32:53 CEST
LastModif Thursday 12 of June, 2014 23:32:53 CEST

Ecole Doctorale Informatique Paris-Sud

Nicole Bidoit
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 at lri.fr