Springenberg, Jost Tobias

32 publications

CoRL 2025 $\pi_0.5$: A Vision-Language-Action Model with Open-World Generalization Kevin Black, Noah Brown, James Darpinian, Karan Dhabalia, Danny Driess, Adnan Esmail, Michael Robert Equi, Chelsea Finn, Niccolo Fusai, Manuel Y. Galliker, Dibya Ghosh, Lachy Groom, Karol Hausman, Brian Ichter, Szymon Jakubczak, Tim Jones, Liyiming Ke, Devin LeBlanc, Sergey Levine, Adrian Li-Bell, Mohith Mothukuri, Suraj Nair, Karl Pertsch, Allen Z. Ren, Lucy Xiaoyang Shi, Laura Smith, Jost Tobias Springenberg, Kyle Stachowicz, James Tanner, Quan Vuong, Homer Walke, Anna Walling, Haohuan Wang, Lili Yu, Ury Zhilinsky

NeurIPS 2025 Knowledge Insulating Vision-Language-Action Models: Train Fast, Run Fast, Generalize Better Danny Driess, Jost Tobias Springenberg, Brian Ichter, Lili Yu, Adrian Li-Bell, Karl Pertsch, Allen Z. Ren, Homer Walke, Quan Vuong, Lucy Xiaoyang Shi, Sergey Levine

ICLR 2025 Learning from Negative Feedback, or Positive Feedback or Both Abbas Abdolmaleki, Bilal Piot, Bobak Shahriari, Jost Tobias Springenberg, Tim Hertweck, Michael Bloesch, Rishabh Joshi, Thomas Lampe, Junhyuk Oh, Nicolas Heess, Jonas Buchli, Martin Riedmiller

NeurIPS 2024 Imitating Language via Scalable Inverse Reinforcement Learning Markus Wulfmeier, Michael Bloesch, Nino Vieillard, Arun Ahuja, Jörg Bornschein, Sandy Huang, Artem Sokolov, Matt Barnes, Guillaume Desjardins, Alex Bewley, Sarah Maria Elisabeth Bechtle, Jost Tobias Springenberg, Nikola Momchev, Olivier Bachem, Matthieu Geist, Martin Riedmiller

ICML 2024 Offline Actor-Critic Reinforcement Learning Scales to Large Models Jost Tobias Springenberg, Abbas Abdolmaleki, Jingwei Zhang, Oliver Groth, Michael Bloesch, Thomas Lampe, Philemon Brakel, Sarah Maria Elisabeth Bechtle, Steven Kapturowski, Roland Hafner, Nicolas Heess, Martin Riedmiller

TMLR 2024 RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao, Coline Manon Devin, Alex X. Lee, Maria Bauza Villalonga, Todor Davchev, Yuxiang Zhou, Agrim Gupta, Akhil Raju, Antoine Laurens, Claudio Fantacci, Valentin Dalibard, Martina Zambelli, Murilo Fernandes Martins, Rugile Pevceviciute, Michiel Blokzijl, Misha Denil, Nathan Batchelor, Thomas Lampe, Emilio Parisotto, Konrad Zolna, Scott Reed, Sergio Gómez Colmenarejo, Jonathan Scholz, Abbas Abdolmaleki, Oliver Groth, Jean-Baptiste Regli, Oleg Sushkov, Thomas Rothörl, Jose Enrique Chen, Yusuf Aytar, David Barker, Joy Ortiz, Martin Riedmiller, Jost Tobias Springenberg, Raia Hadsell, Francesco Nori, Nicolas Heess

TMLR 2022 A Generalist Agent Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gómez Colmenarejo, Alexander Novikov, Gabriel Barth-maron, Mai Giménez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, Tom Eccles, Jake Bruce, Ali Razavi, Ashley Edwards, Nicolas Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, Nando de Freitas

ICLR 2022 Evaluating Model-Based Planning and Planner Amortization for Continuous Control Arunkumar Byravan, Leonard Hasenclever, Piotr Trochim, Mehdi Mirza, Alessandro Davide Ialongo, Yuval Tassa, Jost Tobias Springenberg, Abbas Abdolmaleki, Nicolas Heess, Josh Merel, Martin Riedmiller

CoRL 2021 Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes Alex X. Lee, Coline Manon 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 Collect & Infer - A Fresh Look at Data-Efficient Reinforcement Learning Martin Riedmiller, Jost Tobias Springenberg, Roland Hafner, Nicolas Heess

NeurIPS 2020 Critic Regularized Regression Ziyu Wang, Alexander Novikov, Konrad Zolna, Josh S Merel, Jost Tobias Springenberg, Scott E Reed, Bobak Shahriari, Noah Siegel, Caglar Gulcehre, Nicolas Heess, Nando de Freitas

ICLR 2020 Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning Noah Siegel, Jost Tobias Springenberg, Felix Berkenkamp, Abbas Abdolmaleki, Michael Neunert, Thomas Lampe, Roland Hafner, Nicolas Heess, Martin Riedmiller

CoRL 2020 Learning Dexterous Manipulation from Suboptimal Experts Rae Jeong, Jost Tobias Springenberg, Jackie Kay, Dan Zheng, Alexandre Galashov, Nicolas Heess, Francesco Nori

ICLR 2020 Robust Reinforcement Learning for Continuous Control with Model Misspecification Daniel J. Mankowitz, Nir Levine, Rae Jeong, Yuanyuan Shi, Jackie Kay, Abbas Abdolmaleki, Jost Tobias Springenberg, Timothy Mann, Todd Hester, Martin Riedmiller

NeurIPS 2020 Training Generative Adversarial Networks by Solving Ordinary Differential Equations Chongli Qin, Yan Wu, Jost Tobias Springenberg, Andy Brock, Jeff Donahue, Timothy Lillicrap, Pushmeet Kohli

ICLR 2020 V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control 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

CoRL 2019 Imagined Value Gradients: Model-Based Policy Optimization with Tranferable Latent Dynamics Models Arunkumar Byravan, Jost Tobias Springenberg, Abbas Abdolmaleki, Roland Hafner, Michael Neunert, Thomas Lampe, Noah Siegel, Nicolas Heess, Martin Riedmiller

ICML 2018 Graph Networks as Learnable Physics Engines for Inference and Control Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, Peter Battaglia

ICLR 2018 Learning an Embedding Space for Transferable Robot Skills Karol Hausman, Jost Tobias Springenberg, Ziyu Wang, Nicolas Heess, Martin Riedmiller

ICML 2018 Learning by Playing Solving Sparse Reward Tasks from Scratch Martin Riedmiller, Roland Hafner, Thomas Lampe, Michael Neunert, Jonas Degrave, Tom Wiele, Vlad Mnih, Nicolas Heess, Jost Tobias Springenberg

ICLR 2018 Maximum a Posteriori Policy Optimisation Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Remi Munos, Nicolas Heess, Martin Riedmiller

ICLR 2017 Learning Curve Prediction with Bayesian Neural Networks Aaron Klein, Stefan Falkner, Jost Tobias Springenberg, Frank Hutter

NeurIPS 2016 Bayesian Optimization with Robust Bayesian Neural Networks Jost Tobias Springenberg, Aaron Klein, Stefan Falkner, Frank Hutter

AutoML 2016 Towards Automatically-Tuned Neural Networks Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, Frank Hutter

ICLR 2016 Unsupervised and Semi-Supervised Learning with Categorical Generative Adversarial Networks Jost Tobias Springenberg

AAAI 2015 Initializing Bayesian Hyperparameter Optimization via Meta-Learning Matthias Feurer, Jost Tobias Springenberg, Frank Hutter

CVPR 2015 Learning to Generate Chairs with Convolutional Neural Networks Alexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox

IJCAI 2015 Speeding up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves Tobias Domhan, Jost Tobias Springenberg, Frank Hutter

ICLR 2015 Striving for Simplicity: The All Convolutional Net Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, Martin A. Riedmiller

NeurIPS 2014 Discriminative Unsupervised Feature Learning with Convolutional Neural Networks Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller, Thomas Brox

ICLR 2014 Improving Deep Neural Networks with Probabilistic Maxout Units Jost Tobias Springenberg, Martin A. Riedmiller

ICLR 2014 Unsupervised Feature Learning by Augmenting Single Images Alexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox