Michael Neunert | DeepAI

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Featured Co-authors

Nicolas Heess
104 publications
Marco Hutter
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Wenhao Yu
66 publications
Ofir Nachum
63 publications
Martin Riedmiller
52 publications
Jie Tan
50 publications
Vikas Sindhwani
45 publications
Raia Hadsell
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Jost Tobias Springenberg
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Markus Wulfmeier
33 publications
Abbas Abdolmaleki
32 publications

∙ 07/21/2023

Towards practical reinforcement learning for tokamak magnetic control

Reinforcement learning (RL) has shown promising results for real-time co...

0 Brendan D. Tracey, et al. ∙

∙ 05/24/2023

Barkour: Benchmarking Animal-level Agility with Quadruped Robots

Animals have evolved various agile locomotion strategies, such as sprint...

2 Ken Caluwaerts, et al. ∙

∙ 11/24/2022

SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration

The ability to effectively reuse prior knowledge is a key requirement wh...

0 Giulia Vezzani, et al. ∙

∙ 03/31/2022

Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors

We investigate the use of prior knowledge of human and animal movement t...

0 Steven Bohez, et al. ∙

∙ 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. ∙

∙ 10/29/2020

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

Robot manipulation requires a complex set of skills that need to be care...

0 Giulia Vezzani, et al. ∙

∙ 08/06/2020

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

Modern Reinforcement Learning (RL) algorithms promise to solve difficult...

0 Roland Hafner, et al. ∙

∙ 07/30/2020

Data-efficient Hindsight Off-policy Option Learning

Solutions to most complex tasks can be decomposed into simpler, intermed...

38 Markus Wulfmeier, et al. ∙

∙ 05/15/2020

A Distributional View on Multi-Objective Policy Optimization

Many real-world problems require trading off multiple competing objectiv...

0 Abbas Abdolmaleki, et al. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 02/13/2019

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

We present a method for fast training of vision based control policies o...

2 Devin Schwab, et al. ∙

∙ 02/12/2019

Value constrained model-free continuous control

The naive application of Reinforcement Learning algorithms to continuous...

0 Steven Bohez, et al. ∙

∙ 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. ∙

∙ 01/12/2018

The Control Toolbox - An Open-Source C++ Library for Robotics, Optimal and Model Predictive Control

We introduce the Control Toolbox (CT), an open-source C++ library for ef...

0 Markus Giftthaler, et al. ∙

∙ 12/07/2017

Whole-Body Nonlinear Model Predictive Control Through Contacts for Quadrupeds

In this work we present a whole-body Nonlinear Model Predictive Control ...

0 Michael Neunert, et al. ∙

∙ 11/29/2017

A Family of Iterative Gauss-Newton Shooting Methods for Nonlinear Optimal Control

This paper introduces a family of iterative algorithms for unconstrained...

0 Markus Giftthaler, et al. ∙

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