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NeuralSim: Augmenting Differentiable Simulators with Neural Networks

Nov 09, 2020
Eric Heiden, David Millard, Erwin Coumans, Yizhou Sheng, Gaurav S. Sukhatme

* Submitted to ICRA 2021 

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Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments

Oct 22, 2020
Jun Yamada, Youngwoon Lee, Gautam Salhotra, Karl Pertsch, Max Pflueger, Gaurav S. Sukhatme, Joseph J. Lim, Peter Englert

* Published at the Conference on Robot Learning (CoRL) 2020 

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Learning Equality Constraints for Motion Planning on Manifolds

Sep 24, 2020
Giovanni Sutanto, Isabel M. Rayas Fernández, Peter Englert, Ragesh K. Ramachandran, Gaurav S. Sukhatme


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Resilient Monitoring in Heterogeneous Multi-robot Systems through Network Reconfiguration

Aug 04, 2020
Ragesh K. Ramachandran, Pietro Pierpaoli, Magnus Egerstedt, Gaurav S. Sukhatme

* 12 pages, 5 figures 

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Augmenting Differentiable Simulators with Neural Networks to Close the Sim2Real Gap

Jul 12, 2020
Eric Heiden, David Millard, Erwin Coumans, Gaurav S. Sukhatme


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Learning Manifolds for Sequential Motion Planning

Jul 04, 2020
Isabel M. Rayas Fernández, Giovanni Sutanto, Peter Englert, Ragesh K. Ramachandran, Gaurav S. Sukhatme

* Accepted for presentation at the Robotics: Science and Systems (RSS) 2020 Workshop for Learning (in) Task and Motion Planning. Paper length is 4 pages (i.e. 3 pages of technical content and 1 page of the references) 

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Supervised Learning and Reinforcement Learning of Feedback Models for Reactive Behaviors: Tactile Feedback Testbed

Jun 29, 2020
Giovanni Sutanto, Katharina Rombach, Yevgen Chebotar, Zhe Su, Stefan Schaal, Gaurav S. Sukhatme, Franziska Meier

* Submitted to the International Journal of Robotics Research. Paper length is 21 pages (including references) with 12 figures. A video overview of the reinforcement learning experiment on the real robot can be seen at https://www.youtube.com/watch?v=WDq1rcupVM0. arXiv admin note: text overlap with arXiv:1710.08555 

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Confidence-rich grid mapping

Jun 29, 2020
Ali-akbar Agha-mohammadi, Eric Heiden, Karol Hausman, Gaurav S. Sukhatme

* The International Journal of Robotics Research, 38(12-13), 1352-1374 (2019) 
* Published at International Journal of Robotics Research (IJRR) 2019 (https://journals.sagepub.com/doi/10.1177/0278364919839762

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Sampling-Based Motion Planning on Manifold Sequences

Jun 03, 2020
Peter Englert, Isabel M. Rayas Fernández, Ragesh K. Ramachandran, Gaurav S. Sukhatme


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Plan-Space State Embeddings for Improved Reinforcement Learning

Apr 30, 2020
Max Pflueger, Gaurav S. Sukhatme

* Submitted to IROS 2020 

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Efficient Adaptation for End-to-End Vision-Based Robotic Manipulation

Apr 21, 2020
Ryan Julian, Benjamin Swanson, Gaurav S. Sukhatme, Sergey Levine, Chelsea Finn, Karol Hausman

* 8.5 pages, 9 figures. See video overview and experiments at https://youtu.be/pPDVewcSpdc and project website at https://ryanjulian.me/continual-fine-tuning 

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Resilience in multi-robot multi-target tracking with unknown number of targets through reconfiguration

Apr 15, 2020
Ragesh K. Ramachandran, Nicole Fronda, Gaurav S. Sukhatme

* 21 pages, 4 figures 

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On Localizing a Camera from a Single Image

Mar 24, 2020
Pradipta Ghosh, Xiaochen Liu, Hang Qiu, Marcos A. M. Vieira, Gaurav S. Sukhatme, Ramesh Govindan


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Experimental Comparison of Global Motion Planning Algorithms for Wheeled Mobile Robots

Mar 07, 2020
Eric Heiden, Luigi Palmieri, Kai O. Arras, Gaurav S. Sukhatme, Sven Koenig

* Extended version of manuscript under review 

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Automatic Differentiation and Continuous Sensitivity Analysis of Rigid Body Dynamics

Jan 22, 2020
David Millard, Eric Heiden, Shubham Agrawal, Gaurav S. Sukhatme

* arXiv admin note: substantial text overlap with arXiv:1905.10706 

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Physics-based Simulation of Continuous-Wave LIDAR for Localization, Calibration and Tracking

Dec 03, 2019
Eric Heiden, Ziang Liu, Ragesh K. Ramachandran, Gaurav S. Sukhatme

* Submitted to ICRA 2020 

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Resilient Coverage: Exploring the Local-to-Global Trade-off

Oct 03, 2019
Ragesh K. Ramachandran, Lifeng Zhou, Gaurav S. Sukhatme

* 8 pages, 5 figures, submitted to ACC 2020 

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Resilience in multi-robot target tracking through reconfiguration

Oct 03, 2019
Ragesh K. Ramachandran, Nicole Fronda, Gaurav S. Sukhatme

* 7 pages, 6 figures, submitted to ICRA 2020 

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Interactive Differentiable Simulation

May 26, 2019
Eric Heiden, David Millard, Hejia Zhang, Gaurav S. Sukhatme


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Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotors

Apr 16, 2019
Artem Molchanov, Tao Chen, Wolfgang Hönig, James A. Preiss, Nora Ayanian, Gaurav S. Sukhatme


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Auto-conditioned Recurrent Mixture Density Networks for Learning Generalizable Robot Skills

Mar 20, 2019
Hejia Zhang, Eric Heiden, Stefanos Nikolaidis, Joseph J. Lim, Gaurav S. Sukhatme

* Submitted to IROS 2019 

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Resilience by Reconfiguration: Exploiting Heterogeneity in Robot Teams

Mar 12, 2019
Ragesh K. Ramachandran, James A. Preiss, Gaurav S. Sukhatme

* 8 pages, 6 figures, submitted to 2019 IROS RA-Letter 

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Planning Safe Paths through Hazardous Environments

Nov 12, 2018
Chris Denniston, Thomas R. Krogstad, Stephanie Kemna, Gaurav S. Sukhatme

* Initially created as IROS 2018 submission, but additional appendices to show all results, and updated in Spring 2018 

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Auto-conditioned Recurrent Mixture Density Networks for Complex Trajectory Generation

Oct 02, 2018
Hejia Zhang, Eric Heiden, Ryan Julian, Zhangpeng He, Joseph J. Lim, Gaurav S. Sukhatme

* Submitted to ICRA 2019 

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Data-Driven Learning and Planning for Environmental Sampling

Mar 28, 2018
Kai-Chieh Ma, Lantao Liu, Hordur K. Heidarsson, Gaurav S. Sukhatme

* arXiv admin note: substantial text overlap with arXiv:1609.07560 

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A Solution to Time-Varying Markov Decision Processes

Jan 25, 2018
Lantao Liu, Gaurav S. Sukhatme


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Downwash-Aware Trajectory Planning for Large Quadrotor Teams

Jul 23, 2017
James A. Preiss, Wolfgang Hönig, Nora Ayanian, Gaurav S. Sukhatme

* 8 pages 

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A Spatio-Temporal Representation for the Orienteering Problem with Time-Varying Profits

Jul 02, 2017
Zhibei Ma, Kai Yin, Lantao Liu, Gaurav S. Sukhatme


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