Home /Research /End-to-end nonprehensile rearrangement with deep reinforcement learning and simulation-to-reality transfer

LEARNING

End-to-end nonprehensile rearrangement with deep reinforcement learning and simulation-to-reality transfer

Weihao Yuan, Kaiyu Hang, Danica Kragić, Michael Y. Wang, Johannes A. Stork

Year: 2019
Citations: 54

Keywords

Computer scienceReinforcement learningArtificial intelligenceProcess (computing)RobotSet (abstract data type)Transfer of learningTask (project management)Object (grammar)End-to-end principle

End-to-end nonprehensile rearrangement with deep reinforcement learning and simulation-to-reality transfer

Keywords

Related papers

Statistical Learning Theory

Artificial intelligence: a modern approach

Applied Nonlinear Control

A new optimizer using particle swarm theory