Learning Temporal Coherent Features through Life-Time Sparsity. - Jost Tobias Springenberg; Martin A. Riedmiller (ICONIP (1), 2012)

A learned feature descriptor for object recognition in RGB-D data. - Manuel Blum 002; Jost Tobias Springenberg; Jan Wülfing; Martin A. Riedmiller (ICRA, 2012)

Approximate real-time optimal control based on sparse Gaussian process models. - Joschka Boedecker; Jost Tobias Springenberg; Jan Wülfing; Martin A. Riedmiller (ADPRL, 2014)

Using Meta-Learning to Initialize Bayesian Optimization of Hyperparameters. - Matthias Feurer; Jost Tobias Springenberg; Frank Hutter (MetaSel@ECAI, 2014)

Discriminative Unsupervised Feature Learning with Convolutional Neural Networks. - Alexey Dosovitskiy; Jost Tobias Springenberg; Martin A. Riedmiller; Thomas Brox (NIPS, 2014)

Unsupervised feature learning by augmenting single images. - Alexey Dosovitskiy; Jost Tobias Springenberg; Thomas Brox (ICLR (Workshop Poster), 2014)

Improving Deep Neural Networks with Probabilistic Maxout Units. - Jost Tobias Springenberg; Martin A. Riedmiller (ICLR (Workshop Poster), 2014)

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

Learning to generate chairs with convolutional neural networks. - Alexey Dosovitskiy; Jost Tobias Springenberg; Thomas Brox (CVPR, 2015)

Speeding Up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves. - Tobias Domhan; Jost Tobias Springenberg; Frank Hutter (IJCAI, 2015)

Multimodal deep learning for robust RGB-D object recognition. - Andreas Eitel; Jost Tobias Springenberg; Luciano Spinello; Martin A. Riedmiller; Wolfram Burgard (IROS, 2015)

Efficient and Robust Automated Machine Learning. - Matthias Feurer; Aaron Klein; Katharina Eggensperger; Jost Tobias Springenberg; Manuel Blum 002; Frank Hutter (NIPS, 2015)

Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images. - Manuel Watter; Jost Tobias Springenberg; Joschka Boedecker; Martin A. Riedmiller (NIPS, 2015)

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

Autonomous Learning of State Representations for Control: An Emerging Field Aims to Autonomously Learn State Representations for Reinforcement Learning Agents from Their Real-World Sensor Observations. - Wendelin Böhmer; Jost Tobias Springenberg; Joschka Boedecker; Martin A. Riedmiller; Klaus Obermayer (Künstliche Intell., 2015)

Towards Automatically-Tuned Neural Networks. - Hector Mendoza; Aaron Klein; Matthias Feurer; Jost Tobias Springenberg; Frank Hutter (AutoML@ICML, 2016)

Bayesian Optimization with Robust Bayesian Neural Networks. - Jost Tobias Springenberg; Aaron Klein; Stefan Falkner; Frank Hutter (NIPS, 2016)

Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks. - Jost Tobias Springenberg (ICLR (Poster), 2016)

Asynchronous Stochastic Gradient MCMC with Elastic Coupling. - Jost Tobias Springenberg; Aaron Klein; Stefan Falkner; Frank Hutter (CoRR, 2016)

Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks. - Alexey Dosovitskiy; Philipp Fischer; Jost Tobias Springenberg; Martin A. Riedmiller; Thomas Brox (IEEE Trans. Pattern Anal. Mach. Intell., 2016)

Learning Curve Prediction with Bayesian Neural Networks. - Aaron Klein; Stefan Falkner; Jost Tobias Springenberg; Frank Hutter (ICLR (Poster), 2017)

Deep reinforcement learning with successor features for navigation across similar environments. - Jingwei Zhang 001; Jost Tobias Springenberg; Joschka Boedecker; Wolfram Burgard (IROS, 2017)

Deep learning with convolutional neural networks for brain mapping and decoding of movement-related information from the human EEG. - Robin Tibor Schirrmeister; Jost Tobias Springenberg; Lukas Dominique Josef Fiederer; Martin Glasstetter; Katharina Eggensperger; Michael Tangermann; Frank Hutter; Wolfram Burgard; Tonio Ball (CoRR, 2017)

Learning to Generate Chairs, Tables and Cars with Convolutional Networks. - Alexey Dosovitskiy; Jost Tobias Springenberg; Maxim Tatarchenko; Thomas Brox (IEEE Trans. Pattern Anal. Mach. Intell., 2017)

Maximum a Posteriori Policy Optimisation. - Abbas Abdolmaleki; Jost Tobias Springenberg; Yuval Tassa; Rémi Munos; Nicolas Heess; Martin A. Riedmiller (ICLR (Poster), 2018)

Learning an Embedding Space for Transferable Robot Skills. - Karol Hausman; Jost Tobias Springenberg; Ziyu Wang 001; Nicolas Heess; Martin A. Riedmiller (ICLR (Poster), 2018)

Learning by Playing Solving Sparse Reward Tasks from Scratch. - Martin A. Riedmiller; Roland Hafner; Thomas Lampe; Michael Neunert; Jonas Degrave; Tom Van de Wiele; Vlad Mnih; Nicolas Heess; Jost Tobias Springenberg (ICML, 2018)

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

Relative Entropy Regularized Policy Iteration. - Abbas Abdolmaleki; Jost Tobias Springenberg; Jonas Degrave; Steven Bohez; Yuval Tassa; Dan Belov; Nicolas Heess; Martin A. Riedmiller (CoRR, 2018)

Auto-sklearn: Efficient and Robust Automated Machine Learning. - Matthias Feurer; Aaron Klein; Katharina Eggensperger; Jost Tobias Springenberg; Manuel Blum 002; Frank Hutter (Automated Machine Learning, 2019)

Towards Automatically-Tuned Deep Neural Networks. - Hector Mendoza; Aaron Klein; Matthias Feurer; Jost Tobias Springenberg; Matthias Urban; Michael Burkart; Maximilian Dippel; Marius Lindauer; Frank Hutter (Automated Machine Learning, 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 Y. Siegel; Nicolas Heess; Martin A. Riedmiller (CoRL, 2019)

Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics. - Michael Neunert; Abbas Abdolmaleki; Markus Wulfmeier; Thomas Lampe; Jost Tobias Springenberg; Roland Hafner; Francesco Romano; Jonas Buchli; Nicolas Heess; Martin A. Riedmiller (CoRL, 2019)

Simultaneously Learning Vision and Feature-Based Control Policies for Real-World Ball-In-A-Cup. - Devin Schwab; Jost Tobias Springenberg; Murilo Fernandes Martins; Michael Neunert; Thomas Lampe; Abbas Abdolmaleki; Tim Hertweck; Roland Hafner; Francesco Nori; Martin A. Riedmiller (Robotics: Science and Systems, 2019)

Self-supervised Learning of Image Embedding for Continuous Control. - Carlos Florensa; Jonas Degrave; Nicolas Heess; Jost Tobias Springenberg; Martin A. Riedmiller (CoRR, 2019)

Robust Reinforcement Learning for Continuous Control with Model Misspecification. - Daniel J. Mankowitz; Nir Levine; Rae Jeong; Abbas Abdolmaleki; Jost Tobias Springenberg; Timothy A. Mann; Todd Hester; Martin A. Riedmiller (CoRR, 2019)

Regularized Hierarchical Policies for Compositional Transfer in Robotics. - Markus Wulfmeier; Abbas Abdolmaleki; Roland Hafner; Jost Tobias Springenberg; Michael Neunert; Tim Hertweck; Thomas Lampe; Noah Y. Siegel; Nicolas Heess; Martin A. Riedmiller (CoRR, 2019)

Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models. - Arunkumar Byravan; Jost Tobias Springenberg; Abbas Abdolmaleki; Roland Hafner; Michael Neunert; Thomas Lampe; Noah Y. Siegel; Nicolas Heess; Martin A. Riedmiller (CoRR, 2019)

Quinoa: a Q-function You Infer Normalized Over Actions. - Jonas Degrave; Abbas Abdolmaleki; Jost Tobias Springenberg; Nicolas Heess; Martin A. Riedmiller (CoRR, 2019)

Learning Dexterous Manipulation from Suboptimal Experts. - Rae Jeong; Jost Tobias Springenberg; Jackie Kay; Daniel Zheng; Alexandre Galashov; Nicolas Heess; Francesco Nori (CoRL, 2020)

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

Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning. - Noah Y. Siegel; Jost Tobias Springenberg; Felix Berkenkamp; Abbas Abdolmaleki; Michael Neunert; Thomas Lampe; Roland Hafner; Nicolas Heess; Martin A. Riedmiller (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 002; Dhruva Tirumala; Nicolas Heess; Dan Belov; et al. (ICLR, 2020)

Critic Regularized Regression. - Ziyu Wang 001; Alexander Novikov 001; Konrad Zolna; Josh Merel; Jost Tobias Springenberg; Scott E. Reed; Bobak Shahriari; Noah Y. Siegel; Çaglar Gülçehre; Nicolas Heess; Nando de Freitas (NeurIPS, 2020)

Training Generative Adversarial Networks by Solving Ordinary Differential Equations. - Chongli Qin; Yan Wu 010; Jost Tobias Springenberg; Andy Brock; Jeff Donahue; Timothy P. Lillicrap; Pushmeet Kohli (NeurIPS, 2020)

Compositional Transfer in Hierarchical Reinforcement Learning. - Markus Wulfmeier; Abbas Abdolmaleki; Roland Hafner; Jost Tobias Springenberg; Michael Neunert; Noah Y. Siegel; Tim Hertweck; Thomas Lampe; Nicolas Heess; Martin A. Riedmiller (Robotics: Science and Systems, 2020)

Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning. - Noah Y. Siegel; Jost Tobias Springenberg; Felix Berkenkamp; Abbas Abdolmaleki; Michael Neunert; Thomas Lampe; Roland Hafner; Nicolas Heess; Martin A. Riedmiller (CoRR, 2020)

Simple Sensor Intentions for Exploration. - Tim Hertweck; Martin A. Riedmiller; Michael Bloesch; Jost Tobias Springenberg; Noah Y. Siegel; Markus Wulfmeier; Roland Hafner; Nicolas Heess (CoRR, 2020)

Local Search for Policy Iteration in Continuous Control. - Jost Tobias Springenberg; Nicolas Heess; Daniel J. Mankowitz; Josh Merel; Arunkumar Byravan; Abbas Abdolmaleki; Jackie Kay; Jonas Degrave; Julian Schrittwieser; Yuval Tassa; Jonas Buchli; Dan Belov; et al. (CoRR, 2020)

Learning Dexterous Manipulation from Suboptimal Experts. - Rae Jeong; Jost Tobias Springenberg; Jackie Kay; Daniel Zheng; Yuxiang Zhou; Alexandre Galashov; Nicolas Heess; Francesco Nori (CoRR, 2020)

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; José Enrique Chen; et al. (CoRL, 2021)

Collect & Infer - a fresh look at data-efficient Reinforcement Learning. - Martin A. Riedmiller; Jost Tobias Springenberg; Roland Hafner; Nicolas Heess (CoRL, 2021)

Rethinking Exploration for Sample-Efficient Policy Learning. - William F. Whitney; Michael Bloesch; Jost Tobias Springenberg; Abbas Abdolmaleki; Martin A. Riedmiller (CoRR, 2021)

On Multi-objective Policy Optimization as a Tool for Reinforcement Learning. - Abbas Abdolmaleki; Sandy H. Huang; Giulia Vezzani; Bobak Shahriari; Jost Tobias Springenberg; Shruti Mishra; Dhruva TB; Arunkumar Byravan; Konstantinos Bousmalis; András György 001; Csaba Szepesvári; Raia Hadsell; et al. (CoRR, 2021)

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 A. Riedmiller (ICLR, 2022)

How to Spend Your Robot Time: Bridging Kickstarting and Offline Reinforcement Learning for Vision-based Robotic Manipulation. - Alex X. Lee; Coline Devin; Jost Tobias Springenberg; Yuxiang Zhou; Thomas Lampe; Abbas Abdolmaleki; Konstantinos Bousmalis (IROS, 2022)

Revisiting Gaussian mixture critics in off-policy reinforcement learning: a sample-based approach. - Bobak Shahriari; Abbas Abdolmaleki; Arunkumar Byravan; Abe Friesen; Siqi Liu 002; Jost Tobias Springenberg; Nicolas Heess; Matt Hoffman 001; Martin A. Riedmiller (CoRR, 2022)

A Generalist Agent. - Scott E. Reed; Konrad Zolna; Emilio Parisotto; Sergio Gómez Colmenarejo; Alexander Novikov 001; Gabriel Barth-Maron; Mai Gimenez; Yury Sulsky; Jackie Kay; Jost Tobias Springenberg; Tom Eccles; Jake Bruce; et al. (Trans. Mach. Learn. Res., 2022)

Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains. - Jingwei Zhang 001; Jost Tobias Springenberg; Arunkumar Byravan; Leonard Hasenclever; Abbas Abdolmaleki; Dushyant Rao; Nicolas Heess; Martin A. Riedmiller (CoRR, 2023)

A Generalist Dynamics Model for Control. - Ingmar Schubert; Jingwei Zhang 001; Jake Bruce; Sarah Bechtle; Emilio Parisotto; Martin A. Riedmiller; Jost Tobias Springenberg; Arunkumar Byravan; Leonard Hasenclever; Nicolas Heess (CoRR, 2023)

RoboCat: A Self-Improving Foundation Agent for Robotic Manipulation. - Konstantinos Bousmalis; Giulia Vezzani; Dushyant Rao; Coline Devin; Alex X. Lee; Maria Bauzá 001; Todor Davchev; Yuxiang Zhou; Agrim Gupta; Akhil Raju; Antoine Laurens; Claudio Fantacci; et al. (CoRR, 2023)

Offline Actor-Critic Reinforcement Learning Scales to Large Models. - Jost Tobias Springenberg; Abbas Abdolmaleki; Jingwei Zhang 001; Oliver Groth; Michael Bloesch; Thomas Lampe; Philemon Brakel; Sarah Bechtle; Steven Kapturowski; Roland Hafner; Nicolas Heess; Martin A. Riedmiller (ICML, 2024)

Mastering Stacking of Diverse Shapes with Large-Scale Iterative Reinforcement Learning on Real Robots. - Thomas Lampe; Abbas Abdolmaleki; Sarah Bechtle; Sandy H. Huang; Jost Tobias Springenberg; Michael Bloesch; Oliver Groth; Roland Hafner; Tim Hertweck; Michael Neunert; Markus Wulfmeier; Jingwei Zhang 001; et al. (ICRA, 2024)

Imitating Language via Scalable Inverse Reinforcement Learning. - Markus Wulfmeier; Michael Bloesch; Nino Vieillard; Arun Ahuja; Jörg Bornschein; Sandy H. Huang; Artem Sokolov; Matt Barnes 001; Guillaume Desjardins; Alex Bewley; Sarah Bechtle; Jost Tobias Springenberg; et al. (NeurIPS, 2024)

GATS: Gather-Attend-Scatter. - Konrad Zolna; Serkan Cabi; Yutian Chen 001; Eric Lau; Claudio Fantacci; Jurgis Pasukonis; Jost Tobias Springenberg; Sergio Gomez Colmenarejo (CoRR, 2024)

Game On: Towards Language Models as RL Experimenters. - Jingwei Zhang 001; Thomas Lampe; Abbas Abdolmaleki; Jost Tobias Springenberg; Martin A. Riedmiller (CoRR, 2024)

Preference Optimization as Probabilistic Inference. - Abbas Abdolmaleki; Bilal Piot; Bobak Shahriari; Jost Tobias Springenberg; Tim Hertweck; Rishabh Joshi; Junhyuk Oh; Michael Bloesch; Thomas Lampe; Nicolas Heess; Jonas Buchli; Martin A. Riedmiller (CoRR, 2024)

RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation. - Konstantinos Bousmalis; Giulia Vezzani; Dushyant Rao; Coline Manon Devin; Alex X. Lee; Maria Bauzá Villalonga; Todor Davchev; Yuxiang Zhou; Agrim Gupta; Akhil Raju; Antoine Laurens; Claudio Fantacci; et al. (Trans. Mach. Learn. Res., 2024)

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 A. Riedmiller (ICLR, 2025)

Gemini Robotics: Bringing AI into the Physical World. - Gemini Robotics Team; Saminda Abeyruwan; Joshua Ainslie; Jean-Baptiste Alayrac; Montserrat Gonzalez Arenas; Travis Armstrong; Ashwin Balakrishna; Robert Baruch; Maria Bauzá 001; Michiel Blokzijl; Steven Bohez; Konstantinos Bousmalis; et al. (CoRR, 2025)

π0.5: a Vision-Language-Action Model with Open-World Generalization. - Physical Intelligence; Kevin Black; Noah Brown; James Darpinian; Karan Dhabalia; Danny Driess; Adnan Esmail; Michael Equi; Chelsea Finn; Niccolo Fusai; Manuel Y. Galliker; Dibya Ghosh; et al. (CoRR, 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 (CoRR, 2025)

Value from Observations: Towards Large-Scale Imitation Learning via Self-Improvement. - Michael Bloesch; Markus Wulfmeier; Philemon Brakel; Todor Davchev; Martina Zambelli; Jost Tobias Springenberg; Abbas Abdolmaleki; William F. Whitney; Nicolas Heess; Roland Hafner; Martin A. Riedmiller (CoRR, 2025)

Supervised Fine Tuning on Curated Data is Reinforcement Learning (and can be improved). - Chongli Qin; Jost Tobias Springenberg (CoRR, 2025)