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S3K: Self-Supervised Semantic Keypoints for Robotic Manipulation via Multi-View Consistency



Mel Vecerik , Jean-Baptiste Regli , Oleg Sushkov , David Barker , Rugile Pevceviciute , Thomas Rothörl , Christopher Schuster , Raia Hadsell , Lourdes Agapito , Jonathan Scholz

* 11 pages, supplementary material available at: https://sites.google.com/view/2020-s3k/home 

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Improved Exploration through Latent Trajectory Optimization in Deep Deterministic Policy Gradient



Kevin Sebastian Luck , Mel Vecerik , Simon Stepputtis , Heni Ben Amor , Jonathan Scholz

* Accepted for IROS 2019 

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A Framework for Data-Driven Robotics



Serkan Cabi , Sergio Gómez Colmenarejo , Alexander Novikov , Ksenia Konyushkova , Scott Reed , Rae Jeong , Konrad Żołna , Yusuf Aytar , David Budden , Mel Vecerik , Oleg Sushkov , David Barker , Jonathan Scholz , Misha Denil , Nando de Freitas , Ziyu Wang


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Generative predecessor models for sample-efficient imitation learning



Yannick Schroecker , Mel Vecerik , Jonathan Scholz


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Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards



Mel Vecerik , Todd Hester , Jonathan Scholz , Fumin Wang , Olivier Pietquin , Bilal Piot , Nicolas Heess , Thomas Rothörl , Thomas Lampe , Martin Riedmiller


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PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations



Rico Jonschkowski , Roland Hafner , Jonathan Scholz , Martin Riedmiller

* Accepted at Robotics: Science and Systems (RSS 2017) Workshop -- New Frontiers for Deep Learning in Robotics http://juxi.net/workshop/deep-learning-rss-2017/ 

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