Jost Tobias Springenberg

research

∙ 06/20/2023

RoboCat: A Self-Improving Foundation Agent for Robotic Manipulation

The ability to leverage heterogeneous robotic experience from different ...

0 Konstantinos Bousmalis, et al. ∙

research

∙ 05/18/2023

A Generalist Dynamics Model for Control

We investigate the use of transformer sequence models as dynamics models...

0 Ingmar Schubert, et al. ∙

research

∙ 02/24/2023

Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains

In this paper we study the problem of learning multi-step dynamics predi...

0 Jingwei Zhang, et al. ∙

research

∙ 05/12/2022

A Generalist Agent

Inspired by progress in large-scale language modeling, we apply a simila...

12 Scott Reed, et al. ∙

research

∙ 05/06/2022

How to Spend Your Robot Time: Bridging Kickstarting and Offline Reinforcement Learning for Vision-based Robotic Manipulation

Reinforcement learning (RL) has been shown to be effective at learning c...

0 Alex X. Lee, et al. ∙

research

∙ 04/21/2022

Revisiting Gaussian mixture critics in off-policy reinforcement learning: a sample-based approach

Actor-critic algorithms that make use of distributional policy evaluatio...

0 Bobak Shahriari, et al. ∙

research

∙ 10/12/2021

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

We study the problem of robotic stacking with objects of complex geometr...

0 Alex X. Lee, et al. ∙

research

∙ 10/07/2021

Evaluating model-based planning and planner amortization for continuous control

There is a widespread intuition that model-based control methods should ...

0 Arunkumar Byravan, et al. ∙

research

∙ 08/23/2021

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

This position paper proposes a fresh look at Reinforcement Learning (RL)...

0 Martin Riedmiller, et al. ∙

research

∙ 06/15/2021

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

Many advances that have improved the robustness and efficiency of deep r...

0 Abbas Abdolmaleki, et al. ∙

research

∙ 01/23/2021

Rethinking Exploration for Sample-Efficient Policy Learning

Off-policy reinforcement learning for control has made great strides in ...

0 William F. Whitney, et al. ∙

research

∙ 10/28/2020

Training Generative Adversarial Networks by Solving Ordinary Differential Equations

The instability of Generative Adversarial Network (GAN) training has fre...

21 Chongli Qin, et al. ∙

research

∙ 10/16/2020

Learning Dexterous Manipulation from Suboptimal Experts

Learning dexterous manipulation in high-dimensional state-action spaces ...

0 Rae Jeong, et al. ∙

research

∙ 10/12/2020

Local Search for Policy Iteration in Continuous Control

We present an algorithm for local, regularized, policy improvement in re...

0 Jost Tobias Springenberg, et al. ∙

research

∙ 06/26/2020

Critic Regularized Regression

Offline reinforcement learning (RL), also known as batch RL, offers the ...

32 Ziyu Wang, et al. ∙

research

∙ 05/15/2020

Simple Sensor Intentions for Exploration

Modern reinforcement learning algorithms can learn solutions to increasi...

2 Tim Hertweck, et al. ∙

research

∙ 02/19/2020

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

Off-policy reinforcement learning algorithms promise to be applicable in...

7 Noah Y. Siegel, et al. ∙

research

∙ 01/02/2020

Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics

Many real-world control problems involve both discrete decision variable...

15 Michael Neunert, et al. ∙

research

∙ 11/05/2019

Quinoa: a Q-function You Infer Normalized Over Actions

We present an algorithm for learning an approximate action-value soft Q-...

20 Jonas Degrave, et al. ∙

research

∙ 10/09/2019

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

Humans are masters at quickly learning many complex tasks, relying on an...

5 Arunkumar Byravan, et al. ∙

research

∙ 09/26/2019

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

Some of the most successful applications of deep reinforcement learning ...

0 H. Francis Song, et al. ∙

research

∙ 06/26/2019

Regularized Hierarchical Policies for Compositional Transfer in Robotics

The successful application of flexible, general learning algorithms -- s...

1 Markus Wulfmeier, et al. ∙

research

∙ 06/18/2019

Robust Reinforcement Learning for Continuous Control with Model Misspecification

We provide a framework for incorporating robustness -- to perturbations ...

0 Daniel J. Mankowitz, et al. ∙

research

∙ 01/03/2019

Self-supervised Learning of Image Embedding for Continuous Control

Operating directly from raw high dimensional sensory inputs like images ...

0 Carlos Florensa, et al. ∙

research

∙ 12/05/2018

Relative Entropy Regularized Policy Iteration

We present an off-policy actor-critic algorithm for Reinforcement Learni...

2 Abbas Abdolmaleki, et al. ∙

research

∙ 06/14/2018

Maximum a Posteriori Policy Optimisation

We introduce a new algorithm for reinforcement learning called Maximum a...

0 Abbas Abdolmaleki, et al. ∙

research

∙ 06/04/2018

Graph networks as learnable physics engines for inference and control

Understanding and interacting with everyday physical scenes requires ric...

2 Alvaro Sanchez-Gonzalez, et al. ∙

research

∙ 02/28/2018

Learning by Playing - Solving Sparse Reward Tasks from Scratch

We propose Scheduled Auxiliary Control (SAC-X), a new learning paradigm ...

0 Martin Riedmiller, et al. ∙

research

∙ 03/15/2017

Deep learning with convolutional neural networks for EEG decoding and visualization

A revised version of this article is now available at Human Brain Mappin...

0 Robin Tibor Schirrmeister, et al. ∙

research

∙ 12/16/2016

Deep Reinforcement Learning with Successor Features for Navigation across Similar Environments

In this paper we consider the problem of robot navigation in simple maze...

0 Jingwei Zhang, et al. ∙

research

∙ 12/02/2016

Asynchronous Stochastic Gradient MCMC with Elastic Coupling

We consider parallel asynchronous Markov Chain Monte Carlo (MCMC) sampli...

0 Jost Tobias Springenberg, et al. ∙

research

∙ 11/19/2015

Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks

In this paper we present a method for learning a discriminative classifi...

0 Jost Tobias Springenberg, et al. ∙

research

∙ 07/24/2015

Multimodal Deep Learning for Robust RGB-D Object Recognition

Robust object recognition is a crucial ingredient of many, if not all, r...

0 Andreas Eitel, et al. ∙

research

∙ 06/24/2015

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

We introduce Embed to Control (E2C), a method for model learning and con...

0 Manuel Watter, et al. ∙

research

∙ 12/21/2014

Striving for Simplicity: The All Convolutional Net

Most modern convolutional neural networks (CNNs) used for object recogni...

0 Jost Tobias Springenberg, et al. ∙

research

∙ 11/21/2014

Learning to Generate Chairs, Tables and Cars with Convolutional Networks

We train generative 'up-convolutional' neural networks which are able to...

0 Alexey Dosovitskiy, et al. ∙

research

∙ 06/26/2014

Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks

Deep convolutional networks have proven to be very successful in learnin...

0 Alexey Dosovitskiy, et al. ∙

research

∙ 12/20/2013

Improving Deep Neural Networks with Probabilistic Maxout Units

We present a probabilistic variant of the recently introduced maxout uni...

0 Jost Tobias Springenberg, et al. ∙

research

∙ 12/18/2013

Unsupervised feature learning by augmenting single images

When deep learning is applied to visual object recognition, data augment...

0 Alexey Dosovitskiy, et al. ∙

Jost Tobias Springenberg

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