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Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes


Oct 12, 2021
Alex X. Lee, Coline Devin, Yuxiang Zhou, Thomas Lampe, Konstantinos Bousmalis, Jost Tobias Springenberg, Arunkumar Byravan, Abbas Abdolmaleki, Nimrod Gileadi, David Khosid, Claudio Fantacci, Jose Enrique Chen, Akhil Raju, Rae Jeong, Michael Neunert, Antoine Laurens, Stefano Saliceti, Federico Casarini, Martin Riedmiller, Raia Hadsell, Francesco Nori

* CoRL 2021. Video: https://dpmd.ai/robotics-stacking-YT . Blog: https://dpmd.ai/robotics-stacking . Code: https://github.com/deepmind/rgb_stacking 

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Evaluating model-based planning and planner amortization for continuous control


Oct 07, 2021
Arunkumar Byravan, Leonard Hasenclever, Piotr Trochim, Mehdi Mirza, Alessandro Davide Ialongo, Yuval Tassa, Jost Tobias Springenberg, Abbas Abdolmaleki, Nicolas Heess, Josh Merel, Martin Riedmiller

* 9 pages main text, 30 pages with references and appendix including several ablations and additional experiments. Submitted to ICLR 2022 

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Is Curiosity All You Need? On the Utility of Emergent Behaviours from Curious Exploration


Sep 17, 2021
Oliver Groth, Markus Wulfmeier, Giulia Vezzani, Vibhavari Dasagi, Tim Hertweck, Roland Hafner, Nicolas Heess, Martin Riedmiller

* 14 pages, 7 figures, 2 tables 

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Collect & Infer -- a fresh look at data-efficient Reinforcement Learning


Aug 23, 2021
Martin Riedmiller, Jost Tobias Springenberg, Roland Hafner, Nicolas Heess


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On Multi-objective Policy Optimization as a Tool for Reinforcement Learning


Jun 15, 2021
Abbas Abdolmaleki, Sandy H. Huang, Giulia Vezzani, Bobak Shahriari, Jost Tobias Springenberg, Shruti Mishra, Dhruva TB, Arunkumar Byravan, Konstantinos Bousmalis, Andras Gyorgy, Csaba Szepesvari, Raia Hadsell, Nicolas Heess, Martin Riedmiller


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Rethinking Exploration for Sample-Efficient Policy Learning


Jan 23, 2021
William F. Whitney, Michael Bloesch, Jost Tobias Springenberg, Abbas Abdolmaleki, Martin Riedmiller


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Representation Matters: Improving Perception and Exploration for Robotics


Nov 03, 2020
Markus Wulfmeier, Arunkumar Byravan, Tim Hertweck, Irina Higgins, Ankush Gupta, Tejas Kulkarni, Malcolm Reynolds, Denis Teplyashin, Roland Hafner, Thomas Lampe, Martin Riedmiller


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"What, not how": Solving an under-actuated insertion task from scratch


Oct 30, 2020
Giulia Vezzani, Michael Neunert, Markus Wulfmeier, Rae Jeong, Thomas Lampe, Noah Siegel, Roland Hafner, Abbas Abdolmaleki, Martin Riedmiller, Francesco Nori


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Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification


Oct 20, 2020
Daniel J. Mankowitz, Dan A. Calian, Rae Jeong, Cosmin Paduraru, Nicolas Heess, Sumanth Dathathri, Martin Riedmiller, Timothy Mann


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Local Search for Policy Iteration in Continuous Control


Oct 12, 2020
Jost Tobias Springenberg, Nicolas Heess, Daniel Mankowitz, Josh Merel, Arunkumar Byravan, Abbas Abdolmaleki, Jackie Kay, Jonas Degrave, Julian Schrittwieser, Yuval Tassa, Jonas Buchli, Dan Belov, Martin Riedmiller


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Towards General and Autonomous Learning of Core Skills: A Case Study in Locomotion


Aug 06, 2020
Roland Hafner, Tim Hertweck, Philipp Klöppner, Michael Bloesch, Michael Neunert, Markus Wulfmeier, Saran Tunyasuvunakool, Nicolas Heess, Martin Riedmiller


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Data-efficient Hindsight Off-policy Option Learning


Jul 30, 2020
Markus Wulfmeier, Dushyant Rao, Roland Hafner, Thomas Lampe, Abbas Abdolmaleki, Tim Hertweck, Michael Neunert, Dhruva Tirumala, Noah Siegel, Nicolas Heess, Martin Riedmiller


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Simple Sensor Intentions for Exploration


May 15, 2020
Tim Hertweck, Martin Riedmiller, Michael Bloesch, Jost Tobias Springenberg, Noah Siegel, Markus Wulfmeier, Roland Hafner, Nicolas Heess


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A Distributional View on Multi-Objective Policy Optimization


May 15, 2020
Abbas Abdolmaleki, Sandy H. Huang, Leonard Hasenclever, Michael Neunert, H. Francis Song, Martina Zambelli, Murilo F. Martins, Nicolas Heess, Raia Hadsell, Martin Riedmiller


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Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning


Feb 23, 2020
Noah Y. Siegel, Jost Tobias Springenberg, Felix Berkenkamp, Abbas Abdolmaleki, Michael Neunert, Thomas Lampe, Roland Hafner, Nicolas Heess, Martin Riedmiller

* To appear in ICLR 2020 

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Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics


Jan 02, 2020
Michael Neunert, Abbas Abdolmaleki, Markus Wulfmeier, Thomas Lampe, Jost Tobias Springenberg, Roland Hafner, Francesco Romano, Jonas Buchli, Nicolas Heess, Martin Riedmiller

* Presented at the 3rd Conference on Robot Learning (CoRL 2019), Osaka, Japan. Video: https://youtu.be/eUqQDLQXb7I 

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Quinoa: a Q-function You Infer Normalized Over Actions


Nov 05, 2019
Jonas Degrave, Abbas Abdolmaleki, Jost Tobias Springenberg, Nicolas Heess, Martin Riedmiller

* Deep RL Workshop/NeurIPS 

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Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models


Oct 09, 2019
Arunkumar Byravan, Jost Tobias Springenberg, Abbas Abdolmaleki, Roland Hafner, Michael Neunert, Thomas Lampe, Noah Siegel, Nicolas Heess, Martin Riedmiller

* To appear at the 3rd annual Conference on Robot Learning, Osaka, Japan (CoRL 2019). 24 pages including appendix (main paper - 8 pages) 

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V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control


Sep 26, 2019
H. Francis Song, Abbas Abdolmaleki, Jost Tobias Springenberg, Aidan Clark, Hubert Soyer, Jack W. Rae, Seb Noury, Arun Ahuja, Siqi Liu, Dhruva Tirumala, Nicolas Heess, Dan Belov, Martin Riedmiller, Matthew M. Botvinick

* * equal contribution 

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Regularized Hierarchical Policies for Compositional Transfer in Robotics


Jun 27, 2019
Markus Wulfmeier, Abbas Abdolmaleki, Roland Hafner, Jost Tobias Springenberg, Michael Neunert, Tim Hertweck, Thomas Lampe, Noah Siegel, Nicolas Heess, Martin Riedmiller

* Preprint. Under review. Addressed typos 

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Robust Reinforcement Learning for Continuous Control with Model Misspecification


Jun 18, 2019
Daniel J. Mankowitz, Nir Levine, Rae Jeong, Abbas Abdolmaleki, Jost Tobias Springenberg, Timothy Mann, Todd Hester, Martin Riedmiller


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Simultaneously Learning Vision and Feature-based Control Policies for Real-world Ball-in-a-Cup


Feb 18, 2019
Devin Schwab, Tobias Springenberg, Murilo F. Martins, Thomas Lampe, Michael Neunert, Abbas Abdolmaleki, Tim Hertweck, Roland Hafner, Francesco Nori, Martin Riedmiller

* Videos can be found at https://sites.google.com/view/rss-2019-sawyer-bic/ 

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Self-supervised Learning of Image Embedding for Continuous Control


Jan 03, 2019
Carlos Florensa, Jonas Degrave, Nicolas Heess, Jost Tobias Springenberg, Martin Riedmiller

* Contributed talk at Inference to Control workshop at NeurIPS2018 

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Relative Entropy Regularized Policy Iteration


Dec 05, 2018
Abbas Abdolmaleki, Jost Tobias Springenberg, Jonas Degrave, Steven Bohez, Yuval Tassa, Dan Belov, Nicolas Heess, Martin Riedmiller


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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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Maximum a Posteriori Policy Optimisation


Jun 14, 2018
Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Remi Munos, Nicolas Heess, Martin Riedmiller


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Graph networks as learnable physics engines for inference and control


Jun 04, 2018
Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, Peter Battaglia

* ICML 2018 

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Learning by Playing - Solving Sparse Reward Tasks from Scratch


Feb 28, 2018
Martin Riedmiller, Roland Hafner, Thomas Lampe, Michael Neunert, Jonas Degrave, Tom Van de Wiele, Volodymyr Mnih, Nicolas Heess, Jost Tobias Springenberg

* A video of the rich set of learned behaviours can be found at https://youtu.be/mPKyvocNe_M 

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DeepMind Control Suite


Jan 02, 2018
Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, Timothy Lillicrap, Martin Riedmiller

* 24 pages, 7 figures, 2 tables 

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