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Picture for Jonathan Scholz

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


Oct 13, 2020
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


Nov 15, 2019
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


Sep 26, 2019
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


Apr 01, 2019
Yannick Schroecker, Mel Vecerik, Jonathan Scholz


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


Oct 08, 2018
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


Jul 24, 2017
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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