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Representation Matters: Improving Perception and Exploration for Robotics

Nov 03, 2020
Markus Wulfmeier, Arunkumar Byravan, Tim Hertweck, Irina Higgins, Ankush Gupta, Tejas Kulkarni, Malcolm Reynolds, Denis Teplyashin, Roland Hafner, Thomas Lampe, Martin Riedmiller

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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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Motion-Nets: 6D Tracking of Unknown Objects in Unseen Environments using RGB

Oct 30, 2019
Felix Leeb, Arunkumar Byravan, Dieter Fox

* Accepted to IROS 2019 workshop on The Importance of Uncertainty in Deep Learning for Robotics 

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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

* 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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Prospection: Interpretable Plans From Language By Predicting the Future

Mar 20, 2019
Chris Paxton, Yonatan Bisk, Jesse Thomason, Arunkumar Byravan, Dieter Fox

* Accepted to ICRA 2019; extended version with appendix containing additional results 

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SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Planning and Control

Oct 02, 2017
Arunkumar Byravan, Felix Leeb, Franziska Meier, Dieter Fox

* 8 pages, Initial submission to IEEE International Conference on Robotics and Automation (ICRA) 2018 

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SE3-Nets: Learning Rigid Body Motion using Deep Neural Networks

Mar 30, 2017
Arunkumar Byravan, Dieter Fox

* 8 pages. To appear at the IEEE International Conference on Robotics and Automation (ICRA), 2017. V2 Update: Final version submitted to ICRA with experiments testing the robustness of the system to noise and preliminary results on real world data 

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