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Jost Tobias Springenberg

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


Nov 03, 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

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* 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

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* 9 pages main text, 30 pages with references and appendix including several ablations and additional experiments. Submitted to ICLR 2022 
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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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Training Generative Adversarial Networks by Solving Ordinary Differential Equations


Oct 28, 2020
Chongli Qin, Yan Wu, Jost Tobias Springenberg, Andrew Brock, Jeff Donahue, Timothy P. Lillicrap, Pushmeet Kohli

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Learning Dexterous Manipulation from Suboptimal Experts


Oct 16, 2020
Rae Jeong, Jost Tobias Springenberg, Jackie Kay, Daniel Zheng, Yuxiang Zhou, Alexandre Galashov, Nicolas Heess, Francesco Nori

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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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Critic Regularized Regression


Jun 26, 2020
Ziyu Wang, Alexander Novikov, Konrad Ε»oΕ‚na, Jost Tobias Springenberg, Scott Reed, Bobak Shahriari, Noah Siegel, Josh Merel, Caglar Gulcehre, Nicolas Heess, Nando de Freitas

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* 23 pages 
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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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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

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* 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

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* 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

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* 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

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* 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

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* * 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

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* 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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Self-supervised Learning of Image Embedding for Continuous Control


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

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* 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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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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Deep learning with convolutional neural networks for EEG decoding and visualization


Jun 08, 2018
Robin Tibor Schirrmeister, Jost Tobias Springenberg, Lukas Dominique Josef Fiederer, Martin Glasstetter, Katharina Eggensperger, Michael Tangermann, Frank Hutter, Wolfram Burgard, Tonio Ball

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* A revised manuscript (with the new title) has been accepted at Human Brain Mapping, see http://onlinelibrary.wiley.com/doi/10.1002/hbm.23730/full 
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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

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* 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

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* A video of the rich set of learned behaviours can be found at https://youtu.be/mPKyvocNe_M 
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Learning to Generate Chairs, Tables and Cars with Convolutional Networks


Aug 02, 2017
Alexey Dosovitskiy, Jost Tobias Springenberg, Maxim Tatarchenko, Thomas Brox

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* v4: final PAMI version. New architecture figure 
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Deep Reinforcement Learning with Successor Features for Navigation across Similar Environments


Jul 23, 2017
Jingwei Zhang, Jost Tobias Springenberg, Joschka Boedecker, Wolfram Burgard

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* Camera ready version for IROS 2017 
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Asynchronous Stochastic Gradient MCMC with Elastic Coupling


Dec 08, 2016
Jost Tobias Springenberg, Aaron Klein, Stefan Falkner, Frank Hutter

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Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks


Apr 30, 2016
Jost Tobias Springenberg

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Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images


Nov 20, 2015
Manuel Watter, Jost Tobias Springenberg, Joschka Boedecker, Martin Riedmiller

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* Final NIPS version 
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