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Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study


Mar 23, 2021
Jianlan Luo, Oleg Sushkov, Rugile Pevceviciute, Wenzhao Lian, Chang Su, Mel Vecerik, Ning Ye, Stefan Schaal, Jon Scholz


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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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A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning


Oct 08, 2018
Mel Vecerik, Oleg Sushkov, David Barker, Thomas Rothörl, Todd Hester, Jon Scholz


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Sim-to-Real Robot Learning from Pixels with Progressive Nets


May 22, 2018
Andrei A. Rusu, Mel Vecerik, Thomas Rothörl, Nicolas Heess, Razvan Pascanu, Raia Hadsell


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