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CMAX++ : Leveraging Experience in Planning and Execution using Inaccurate Models

Oct 15, 2020
Anirudh Vemula, J. Andrew Bagnell, Maxim Likhachev


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CMAX++ : Leveraging Experience for Planning and Execution using Inaccurate Models

Sep 21, 2020
Anirudh Vemula, J. Andrew Bagnell, Maxim Likhachev


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TRON: A Fast Solver for Trajectory Optimization with Non-Smooth Cost Functions

Apr 01, 2020
Anirudh Vemula, J. Andrew Bagnell

* Submitted to CDC 2020 

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Exploration in Action Space

Mar 31, 2020
Anirudh Vemula, Wen Sun, J. Andrew Bagnell

* Presented at RSS 2018 in Learning and Inference in Robotics: Integrating Structure, Priors and Models workshop. arXiv admin note: text overlap with arXiv:1901.11503 

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Planning and Execution using Inaccurate Models with Provable Guarantees

Mar 15, 2020
Anirudh Vemula, Yash Oza, J. Andrew Bagnell, Maxim Likhachev

* Code at https://github.com/vvanirudh/CMAX and video at https://youtu.be/eQmAeWIhjO8 

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Provably Efficient Imitation Learning from Observation Alone

Jun 11, 2019
Wen Sun, Anirudh Vemula, Byron Boots, J. Andrew Bagnell

* ICML 2019 

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Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective

Jan 31, 2019
Anirudh Vemula, Wen Sun, J. Andrew Bagnell

* Accepted at AISTATS 2019 

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An Algorithmic Perspective on Imitation Learning

Nov 16, 2018
Takayuki Osa, Joni Pajarinen, Gerhard Neumann, J. Andrew Bagnell, Pieter Abbeel, Jan Peters

* 187 pages. Published in Foundations and Trends in Robotics 

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Truncated Horizon Policy Search: Combining Reinforcement Learning & Imitation Learning

May 29, 2018
Wen Sun, J. Andrew Bagnell, Byron Boots

* ICLR 2018 

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Dual Policy Iteration

May 28, 2018
Wen Sun, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell


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Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing

May 25, 2018
Hanzhang Hu, Debadeepta Dey, Martial Hebert, J. Andrew Bagnell


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Log-DenseNet: How to Sparsify a DenseNet

Oct 30, 2017
Hanzhang Hu, Debadeepta Dey, Allison Del Giorno, Martial Hebert, J. Andrew Bagnell


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Predictive-State Decoders: Encoding the Future into Recurrent Networks

Sep 25, 2017
Arun Venkatraman, Nicholas Rhinehart, Wen Sun, Lerrel Pinto, Martial Hebert, Byron Boots, Kris M. Kitani, J. Andrew Bagnell

* NIPS 2017 

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Ignoring Distractors in the Absence of Labels: Optimal Linear Projection to Remove False Positives During Anomaly Detection

Sep 13, 2017
Allison Del Giorno, J. Andrew Bagnell, Martial Hebert

* 13 pages, 6 figures 

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Shared Autonomy via Hindsight Optimization for Teleoperation and Teaming

Jun 01, 2017
Shervin Javdani, Henny Admoni, Stefania Pellegrinelli, Siddhartha S. Srinivasa, J. Andrew Bagnell


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A Fast Stochastic Contact Model for Planar Pushing and Grasping: Theory and Experimental Validation

May 30, 2017
Jiaji Zhou, J. Andrew Bagnell, Matthew T. Mason

* Robotics: Science and Systems 2017 

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Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction

Mar 03, 2017
Wen Sun, Arun Venkatraman, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell

* 17 pages 

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Gradient Boosting on Stochastic Data Streams

Mar 01, 2017
Hanzhang Hu, Wen Sun, Arun Venkatraman, Martial Hebert, J. Andrew Bagnell

* To appear in AISTATS 2017 

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Efficient Feature Group Sequencing for Anytime Linear Prediction

Dec 05, 2016
Hanzhang Hu, Alexander Grubb, J. Andrew Bagnell, Martial Hebert

* Published in UAI 2016, Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, UAI 2016 

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A Discriminative Framework for Anomaly Detection in Large Videos

Sep 28, 2016
Allison Del Giorno, J. Andrew Bagnell, Martial Hebert

* 14 pages without references, 16 pages with. 7 figures. Accepted to ECCV 2016 

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Learning Transferable Policies for Monocular Reactive MAV Control

Aug 01, 2016
Shreyansh Daftry, J. Andrew Bagnell, Martial Hebert

* International Symposium on Experimental Robotics (ISER 2016) 

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Introspective Perception: Learning to Predict Failures in Vision Systems

Jul 28, 2016
Shreyansh Daftry, Sam Zeng, J. Andrew Bagnell, Martial Hebert

* IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016) 

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A Convex Polynomial Force-Motion Model for Planar Sliding: Identification and Application

Jun 16, 2016
Jiaji Zhou, Robert Paolini, J. Andrew Bagnell, Matthew T. Mason

* 2016 IEEE International Conference on Robotics and Automation (ICRA) 

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Learning to Filter with Predictive State Inference Machines

May 30, 2016
Wen Sun, Arun Venkatraman, Byron Boots, J. Andrew Bagnell

* ICML 2016 

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Robust Monocular Flight in Cluttered Outdoor Environments

Apr 16, 2016
Shreyansh Daftry, Sam Zeng, Arbaaz Khan, Debadeepta Dey, Narek Melik-Barkhudarov, J. Andrew Bagnell, Martial Hebert


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Autonomy Infused Teleoperation with Application to BCI Manipulation

Jun 07, 2015
Katharina Muelling, Arun Venkatraman, Jean-Sebastien Valois, John Downey, Jeffrey Weiss, Shervin Javdani, Martial Hebert, Andrew B. Schwartz, Jennifer L. Collinger, J. Andrew Bagnell


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Shared Autonomy via Hindsight Optimization

Apr 17, 2015
Shervin Javdani, Siddhartha S. Srinivasa, J. Andrew Bagnell


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