Home/J. Andrew (Drew) Bagnell

J. Andrew (Drew) Bagnell

Associate Professor
Email: bagnell2@andrew.cmu.edu
Office: NSH 3213
Personal Homepage: http://www.robotwhisperer.org
Administrative Assistant: Jessica Butterbaugh

I am interested in “closing the loop” on complex systems; that is, I am interested in designing algorithms that allow systems to observe their own operation and improve performance. My belief is that the border land between planning, control and computational learning is particularly rich with research challenges and potential to make real, immediate impact on applications. I’m particularly interested in systems for which we can obtain at best a partial model. To this end, I’m excited about extending research tools that come from information theory, statistics, control theory, statistical physics and optimization.

At the moment, I am particularly focused on two areas in machine learning. First I am working on applications of learning and decision making applied to mobile robotics. Second, I am interested in developing rich, structured probabilistic models that are appropriate for both making and learning decisions.

A Probabilistic Planning Framework for Planar Grasping Under Uncertainty
Jiaji Zhou, Robert Paolini, Aaron M. Johnson, J. Andrew (Drew) Bagnell and Matthew T. Mason

Conference Paper, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017), September, 2017
A Fast Stochastic Contact Model for Planar Pushing and Grasping: Theory and Experimental Validation
Jiaji Zhou, J. Andrew (Drew) Bagnell and Matthew T. Mason

Conference Paper, Robotics: Science and Systems, July, 2017
Gradient Boosting on Stochastic Data Streams
Hanzhang Hu, Wen Sun, Arun Venkatraman, Martial Hebert and J. Andrew (Drew) Bagnell

Conference Paper, International Conference on Artificial Intelligence and Statistics (AISTATS 2017), April, 2017
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction
Wen Sun,, Arun Venkatraman, Geoffrey J. Gordon, Byron Boots and J. Andrew (Drew) Bagnell

Tech. Report, CMU-RI-TR-17-05, Robotics Institute, Carnegie Mellon University, March, 2017
Improved Learing of Dynamics for Control
Arun Venkatraman, Roberto Capobianco, Lerrel Pinto, Martial Hebert, Daniele Nardi and J. Andrew (Drew) Bagnell

Conference Paper, International Symposium on Experimental Robotics, October, 2016
A Discriminative Framework for Anomaly Detection in Large Videos
Allison Del Giorno, J. Andrew (Drew) Bagnell and Martial Hebert

Conference Paper, European Conference on Computer Vision (ECCV), No. 2016, October, 2016
A Discriminative Framework for Anomaly Detection in Large Videos
Allison Del Giorno, J. Andrew (Drew) Bagnell and Martial Hebert

Conference Paper, European Conference on Computer Vision (ECCV), October, 2016
Improved Learing of Dynamics for Control
Arun Venkatraman, Roberto Capobianco, Lerrel Pinto, Martial Hebert, Daniele Nardi and J. Andrew (Drew) Bagnell

Conference Paper, International Symposium on Experimental Robotics, October, 2016
Inference Machines for Nonparametric Filter Learning
Arun Venkatraman, Wen Sun, Martial Hebert, Byron Boots and J. Andrew (Drew) Bagnell

Conference Paper, 25th International Joint Conference on Artificial Intelligence (IJCAI-16), July, 2016
Introspective Perception: Learning to Predict Failures in Vision Systems
Shreyansh Daftry, Sam Zeng, J. Andrew (Drew) Bagnell and Martial Hebert

Conference Paper, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016), July, 2016
Inference Machines for Nonparametric Filter Learning
Arun Venkatraman, Wen Sun, Martial Hebert, Byron Boots and J. Andrew (Drew) Bagnell

25th International Joint Conference on Artificial Intelligence (IJCAI-16), July, 2016
Introspective Perception: Learning to Predict Failures in Vision Systems
Shreyansh Daftry, Sam Zeng, J. Andrew (Drew) Bagnell and Martial Hebert

Conference Paper, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016), July, 2016
Learning to Smooth with Bidirectional Predictive State Inference Machines
Wen Sun, Roberto Capobianco, Geoffrey Gordon, J. Andrew (Drew) Bagnell and Byron Boots

Conference Paper, The Conference on Uncertainty in Artificial Intelligence (UAI 2016), June, 2016
Learning to Filter with Predictive State Inference Machines
Wen Sun, Arun Venkatraman, Byron Boots and J. Andrew (Drew) Bagnell

Conference Paper, International Conference on Machine Learning (ICML 2016), June, 2016
A Convex Polynomial Force-Motion Model for Planar Sliding: Identification and Application
Jiaji Zhou, Robert Paolini, J. Andrew (Drew) Bagnell and Matthew T. Mason

Conference Paper, International Conference on Robotics and Automation (ICRA) 2016, May, 2016
Robust Monocular Flight in Cluttered Outdoor Environments
Shreyansh Daftry, Sam Zeng, Arbaaz Khan, Debadeepta Dey, Narek Melik-Barkhudarov, J. Andrew (Drew) Bagnell and Martial Hebert

Conference Paper, ArXiv, April, 2016
Online Bellman Residual and Temporal Difference Algorithms with Predictive Error Guarantees
Wen Sun and J. Andrew (Drew) Bagnell

Conference Paper, The 25th International Joint Conference on Artificial Intelligence - IJCAI 2016, April, 2016
Blending of brain-machine interface and vision-guided autonomous robotics improves neuroprosthetic arm performance during grasping
John E. Downey, Jeffrey M. Weiss, Katharina Muelling, Arun Venkatraman, Jean-Sebastien Valois, Martial Hebert, J. Andrew (Drew) Bagnell, Andrew B. Schwartz and Jennifer L. Collinger

Journal Article, Journal of Neuro Engineering and Rehabilitation, Vol. 13, No. 1, March, 2016
Online Instrumental Variable Regression with Applications to Online Linear System Identification
Arun Venkatraman, Wen Sun, Martial Hebert, J. Andrew (Drew) Bagnell and Byron Boots

Conference Paper, Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February, 2016
Learning Positive Functions in a Hilbert Space
J. Andrew (Drew) Bagnell and Amir-massoud Farahmand

Conference Paper, Carnegie Mellon University, NIPS Workshop on Optimization, (OPT2015), December, 2015
Shared Autonomy via Hindsight Optimization
Shervin Javdani, Siddhartha Srinivasa and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, Proceedings of Robotics: Science and Systems, July, 2015
Theoretical Limits of Speed and Resolution for Kinodynamic Planning in a Poisson Forest
Sanjiban Choudhury, Sebastian Scherer and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, Robotics Science and Systems, July, 2015
Autonomy Infused Teleoperation with Application to BCI Manipulation
Katharina Muelling, Arun Venkatraman, Jean-Sebastien Valois, John Downey, Jeffrey Weiss, Shervin Javdani, Martial Hebert, Andrew B. Schwartz, Jennifer L. Collinger and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, Proceedings of Robotics: Science and Systems, July, 2015
Online Bellman Residual Algorithms with Predictive Error Guarantees
Wen Sun and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, The 31st Conference on Uncertainty in Artificial Intelligence (UAI), July, 2015
Vision and Learning for Deliberative Monocular Cluttered Flight
Debadeepta Dey, Kumar Shaurya Shankar, Sam Zeng, Rupesh Mehta, M. Talha Agcayazi, Christopher Eriksen, Shreyansh Daftry, Martial Hebert and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, Field and Service Robotics (FSR), June, 2015
Semi-Dense Visual Odometry for Monocular Navigation in Cluttered Environment
Shreyansh Daftry, Debadeepta Dey, Harsimrat Sandhawalia, Sam Zeng, J. Andrew (Drew) Bagnell and Martial Hebert

Conference Paper, Carnegie Mellon University, IEEE International Conference on Robotics and Automation (ICRA) workshop on Recent Advances in Sensing and Actuation for Bioinspired Agile Flight, May, 2015
Movement Primitives via Optimization
Anca Dragan, Katharina Muelling, J. Andrew (Drew) Bagnell and Siddhartha Srinivasa

Conference Paper, Carnegie Mellon University, International Conference on Robotics and Automation (ICRA), May, 2015
Visual Chunking: A List Prediction Framework for Region-Based Object Detection
Nicholas Rhinehart, Jiaji Zhou, Martial Hebert and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, IEEE International Conference on Robotics and Automation (ICRA), May, 2015
CHIMP, the CMU Highly Intelligent Mobile Platform
Anthony (Tony) Stentz, Herman Herman, Alonzo Kelly, Eric Meyhofer, Galen Clark Haynes, David Stager, Brian Zajac, J. Andrew (Drew) Bagnell, Jordan Brindza, Christopher Dellin, Michael George, Jose Gonzalez-Mora, Sean Hyde, Morgan Jones, Michel Laverne, Maxim Likhachev, Levi Lister, Matthew D. Powers, Oscar Ramos, Justin Ray, David P. Rice, Justin Scheifflee, Raumi Sidki, Siddhartha Srinivasa, Kyle Strabala, Jean Philippe Tardif, Jean-Sebastien Valois, J Michael Vandeweghe, Michael D. Wagner and Carl Wellington

Journal Article, Carnegie Mellon University, Journal of Field Robotics (JFR), Special Issue: Special issue on DARPA Robotics Challenge (DRC), Vol. 32, No. 2, pp. 209-228, March, 2015
An Invitation to Imitation
J. Andrew (Drew) Bagnell

Tech. Report, CMU-RI-TR-15-08, Robotics Institute, Carnegie Mellon University, March, 2015
A Unified View of Large-scale Zero-sum Equilibrium Computation
Kevin Waugh and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, AAAI Workshop on Computer Poker and Imperfect Information, March, 2015
Approximate MaxEnt Inverse Optimal Control and its Application for Mental Simulation of Human Interactions
De-An Huang, Amir-massoud Farahmand, Kris M. Kitani and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, AAAI Conference on Artificial Intelligence, January, 2015
Submodular Surrogates for Value of Information
Yuxin Chen, Shervin Javdani, Amin Karbasi, J. Andrew (Drew) Bagnell, Siddhartha Srinivasa and Andreas Krause

Conference Paper, Carnegie Mellon University, The Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15), January, 2015
Improving Multi-step Prediction of Learned Time Series Models
Arun Venkatraman, Martial Hebert and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15), January, 2015
Learning to Manipulate Unknown Objects in Clutter by Reinforcement
Abdeslam Boularias, J. Andrew (Drew) Bagnell and Anthony (Tony) Stentz

Conference Paper, Carnegie Mellon University, Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI), January, 2015
A Unified View of Large-scale Zero-sum Equilibrium Computation
Kevin Waugh and J. Andrew (Drew) Bagnell

Conference Paper, AAAI Workshop on Computer Poker and Imperfect Information, January, 2015
Solving Games with Functional Regret Estimation
Kevin Waugh, Dustin Morrill, J. Andrew (Drew) Bagnell and Michael Bowling

Conference Paper, AAAI Conference on Artificial Intelligence, January, 2015
Efficient Optimization for Autonomous Robotic Manipulation of Natural Objects
Abdeslam Boularias, J. Andrew (Drew) Bagnell and Anthony (Tony) Stentz

Conference Paper, Carnegie Mellon University, Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI), pp. 2520-2526, November, 2014
Pose Machines: Articulated Pose Estimation via Inference Machines
Varun Ramakrishna, Daniel Munoz, Martial Hebert, J. Andrew (Drew) Bagnell and Yaser Ajmal Sheikh

Conference Paper, Carnegie Mellon University, European Conference on Computer Vision, July, 2014
Near Optimal Bayesian Active Learning for Decision Making
Shervin Javdani, Yuxin Chen, Amin Karbasi, Andreas Krause, J. Andrew (Drew) Bagnell and Siddhartha Srinivasa

Tech. Report, CMU-RI-TR-14-03, Robotics Institute, Carnegie Mellon University, April, 2014
Near Optimal Bayesian Active Learning for Decision Making
Shervin Javdani, Yuxin Chen, Amin Karbasi, Andreas Krause, J. Andrew (Drew) Bagnell and Siddhartha Srinivasa

Conference Paper, Carnegie Mellon University, Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS), April, 2014
Human-Inspired Force Compliant Grasping Primitives
Moslem Kazemi, Jean-Sebastien Valois, J. Andrew (Drew) Bagnell and Nancy Pollard

Journal Article, Carnegie Mellon University, Autonomous Robots, March, 2014
Reinforcement Learning in Robotics: A Survey
J. Kober, J. Andrew (Drew) Bagnell and J. Peters

Journal Article, Carnegie Mellon University, International Journal of Robotics Research, July, 2013
Perceiving, Learning, and Exploiting Object Affordances for Autonomous Pile Manipulation
Dov Katz, Arun Venkatraman, Moslem Kazemi, J. Andrew (Drew) Bagnell and Anthony (Tony) Stentz

Conference Paper, Carnegie Mellon University, Robotics: Science and Systems Conference, June, 2013
Learning Policies for Contextual Submodular Prediction
Stephane Ross, Jiaji Zhou, Yisong Yue, Debadeepta Dey and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, The 30th International Conference on Machine Learning (ICML 2013), June, 2013
Knapsack Constrained Contextual Submodular List Prediction with Application to Multi-document Summarization
Jiaji Zhou, Stephane Ross, Yisong Yue, Debadeepta Dey and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, ICML 2013 Workshop on Inferning: Interactions between Inference and Learning, June, 2013
Efficient Temporal Consistency for Streaming Video Scene Analysis
Ondrej Miksik, Daniel Munoz, J. Andrew (Drew) Bagnell and Martial Hebert

Conference Paper, Carnegie Mellon University, IEEE International Conference on Robotics and Automation (ICRA), May, 2013
Efficient 3-D Scene Analysis from Streaming Data
Hanzhang Hu, Daniel Munoz, J. Andrew (Drew) Bagnell and Martial Hebert

Conference Paper, Carnegie Mellon University, IEEE International Conference on Robotics and Automation (ICRA), May, 2013
Interactive Segmentation, Tracking, and Kinematic Modeling of Unknown 3D Articulated Objects
Dov Katz, Moslem Kazemi, J. Andrew (Drew) Bagnell and Anthony (Tony) Stentz

Conference Paper, Carnegie Mellon University, Proceedings of IEEE International Conference on Robotics and Automation, May, 2013
Efficient Touch Based Localization through Submodularity
Shervin Javdani, Matthew Klingensmith, J. Andrew (Drew) Bagnell, Nancy Pollard and Siddhartha Srinivasa

Conference Paper, Carnegie Mellon University, IEEE International Conference on Robotics and Automation (ICRA), May, 2013
CHOMP: Covariant Hamiltonian Optimization for Motion Planning
Matthew Zucker, Nathan Ratliff, Anca Dragan, Mihail Pivtoraiko, Matthew Klingensmith, Christopher Dellin, J. Andrew (Drew) Bagnell and Siddhartha Srinivasa

Journal Article, Carnegie Mellon University, International Journal of Robotics Research, May, 2013
Closed-loop Servoing using Real-time Markerless Arm Tracking
Matthew Klingensmith, Thomas Galluzzo, Christopher Dellin, Moslem Kazemi, J. Andrew (Drew) Bagnell and Nancy Pollard

Conference Paper, Carnegie Mellon University, International Conference on Robotics And Automation (Humanoids Workshop), May, 2013
An Architecture for Online Semantic Labeling on UGVs
Arne Suppe, Luis Ernesto Navarro-Serment, Daniel Munoz, Drew Bagnell, Arne Suppe, Luis Ernesto Navarro-Serment, Daniel Munoz, J. Andrew (Drew) Bagnell and Martial Hebert

Conference Paper, Carnegie Mellon University, Proc. SPIE 8741, Unmanned Systems Technology XV, April, 2013
Clearing a Pile of Unknown Objects using Interactive Perception
Dov Katz, Moslem Kazemi, J. Andrew (Drew) Bagnell and Anthony (Tony) Stentz

Conference Paper, Carnegie Mellon University, Proceedings of IEEE International Conference on Robotics and Automation, March, 2013
Learning Monocular Reactive UAV Control in Cluttered Natural Environments
Stephane Ross, Narek Melik-Barkhudarov, Kumar Shaurya Shankar, Andreas Wendel, Debadeepta Dey, J. Andrew (Drew) Bagnell and Martial Hebert

Conference Paper, Carnegie Mellon University, IEEE International Conference on Robotics and Automation, March, 2013
The Principle of Maximum Causal Entropy for Estimating Interacting Processes
Brian D. Ziebart, J. Andrew (Drew) Bagnell and Anind Dey

Journal Article, Carnegie Mellon University, IEEE Transactions on Information Theory, February, 2013
Learning Monocular Reactive UAV Control in Cluttered Natural Environments
Stephane Ross, Narek Melik-Barkhudarov, Kumar Shaurya Shankar, Andreas Wendel, Debadeepta Dey, J. Andrew (Drew) Bagnell and Martial Hebert

Conference Paper, Carnegie Mellon University, No. 1211.169, November, 2012
Clearing a Pile of Unknown Objects using Interactive Perception
Dov Katz, Moslem Kazemi, J. Andrew (Drew) Bagnell and Anthony (Tony) Stentz

Tech. Report, CMU-RI-TR-12-34, Robotics Institute, Carnegie Mellon University, November, 2012
Semi-Autonomous Manipulation of Natural Objects
Dov Katz, Moslem Kazemi, J. Andrew (Drew) Bagnell and Anthony (Tony) Stentz

Tech. Report, CMU-RI-TR-12-33, Robotics Institute, Carnegie Mellon University, November, 2012
Co-inference for Multi-modal Scene Analysis
Daniel Munoz, J. Andrew (Drew) Bagnell and Martial Hebert

Conference Paper, Carnegie Mellon University, European Conference on Computer Vision (ECCV), October, 2012
Activity Forecasting
Kris M. Kitani, Brian D. Ziebart, J. Andrew (Drew) Bagnell and Martial Hebert

Conference Paper, Carnegie Mellon University, European Conference on Computer Vision, October, 2012
Detecting Interesting Events using Unsupervised Density Ratio Estimation
Yuichi Ito, Kris M. Kitani, J. Andrew (Drew) Bagnell and Martial Hebert

Conference Paper, Carnegie Mellon University, 3rd IEEE International Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Streams at ECCV2012, October, 2012
An Integrated System for Autonomous Robotics Manipulation
J. Andrew (Drew) Bagnell, Felipe Cavalcanti, Lei Cui, Thomas Galluzzo, Martial Hebert, Moslem Kazemi, Matthew Klingensmith, Jacqueline Libby, Tommy Liu, Nancy Pollard, Mikhail Pivtoraiko, Jean-Sebastien Valois and Ranqi Zhu

Conference Paper, Carnegie Mellon University, IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2955-2962, October, 2012
Contextual Sequence Optimization with Application to Control Library Optimization
Debadeepta Dey, Tommy Liu, Martial Hebert and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, Robotics Science and Systems, August, 2012
Efficient Touch Based Localization through Submodularity
Shervin Javdani, Matthew Klingensmith, J. Andrew (Drew) Bagnell, Nancy Pollard and Siddhartha Srinivasa

Tech. Report, CMU-RI-TR-12-25, Robotics Institute, Carnegie Mellon University, August, 2012
Robust Object Grasping using Force Compliant Motion Primitives
Moslem Kazemi, Jean-Sebastien Valois, J. Andrew (Drew) Bagnell and Nancy Pollard

Conference Paper, Carnegie Mellon University, Robotics: Science and Systems, July, 2012
Efficient Optimization of Control Libraries
Debadeepta Dey, Tommy Liu, Boris Sofman and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, 26th Conference of Association for Advancement of Artificial Intelligence, July, 2012
Agnostic System Identification for Model-Based Reinforcement Learning
Stephane Ross and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, Appearing in Proceedings of the 29th International Conference on Machine Learning, July, 2012
Learning Autonomous Driving Styles and Maneuvers from Expert Demonstration
David Silver, J. Andrew (Drew) Bagnell and Anthony (Tony) Stentz

Conference Paper, Carnegie Mellon University, International Symposium on Experimental Robotics, June, 2012
Active Learning from Demonstration for Robust Autonomous Navigation
David Silver, J. Andrew (Drew) Bagnell and Anthony (Tony) Stentz

Conference Paper, Carnegie Mellon University, IEEE Conference on Robotics and Automation, May, 2012
SpeedBoost: Anytime Prediction with Uniform Near-Optimality
Alexander Grubb and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, Fifteenth International Conference on Artificial Intelligence and Statistics, April, 2012
Interactive Segmentation, Tracking, and Kinematic Modeling of Unknown Articulated Objects
Dov Katz, Moslem Kazemi, J. Andrew (Drew) Bagnell and Anthony (Tony) Stentz

Tech. Report, CMU-RI-TR-12-06, Robotics Institute, Carnegie Mellon University, March, 2012
Predicting Contextual Sequences via Submodular Function Maximization
Debadeepta Dey, Tommy Liu, Martial Hebert and J. Andrew (Drew) Bagnell

Tech. Report, CMU-RI-TR-12-05, Robotics Institute, Carnegie Mellon University, February, 2012
Reinforcement Planning: RL for Optimal Planners
Matthew Zucker and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, IEEE International Conference on Robotics and Automation (ICRA), February, 2012
Probabilistic Pointing Target Prediction via Inverse Optimal Control
Brian D. Ziebart, Anind Dey and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, International Conference on Intelligent User Interfaces (IUI 2012), February, 2012
Robust Object Grasping using Force Compliant Motion Primitives
Moslem Kazemi, Jean-Sebastien Valois, J. Andrew (Drew) Bagnell and Nancy Pollard

Tech. Report, CMU-RI-TR-12-04, Robotics Institute, Carnegie Mellon University, January, 2012
Learning Message-Passing Inference Machines for Structured Prediction
Stephane Ross, Daniel Munoz, Martial Hebert and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June, 2011
Computational Rationalization: The Inverse Equilibrium Problem
Kevin Waugh, Brian D. Ziebart and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, Proceedings of the International Conference on Machine Learning, June, 2011
Efficient Optimization of Control Libraries
Debadeepta Dey, Tommy Liu, Boris Sofman and J. Andrew (Drew) Bagnell

Tech. Report, CMU-RI-TR-11-20, Robotics Institute, Carnegie Mellon University, June, 2011
3-D Scene Analysis via Sequenced Predictions over Points and Regions
Xuehan Xiong, Daniel Munoz, J. Andrew (Drew) Bagnell and Martial Hebert

Conference Paper, Carnegie Mellon University, IEEE International Conference on Robotics and Automation (ICRA), May, 2011
Segmentation-Based Online Change Detection for Mobile Robots
Bradford Neuman, Boris Sofman, Anthony (Tony) Stentz and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, International Conference on Robotics and Automation, May, 2011
Maximum Causal Entropy Correlated Equilibria for Markov Games
Brian D. Ziebart, J. Andrew (Drew) Bagnell and Anind Dey

Conference Paper, Carnegie Mellon University, International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011), May, 2011
Generalized Boosting Algorithms for Convex Optimization
Alexander Grubb and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, Proceedings of the 28th International Conference on Machine Learning, May, 2011
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning
Stephane Ross, Geoffrey Gordon and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, Proceedings of the 14th International Conference on Artifical Intelligence and Statistics (AISTATS), April, 2011
Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain
David Silver, J. Andrew (Drew) Bagnell and Anthony (Tony) Stentz

Journal Article, Carnegie Mellon University, International Journal of Robotics Research, Vol. 29, No. 12, pp. 1565 - 1592, October, 2010
Stacked Hierarchical Labeling
Daniel Munoz, J. Andrew (Drew) Bagnell and Martial Hebert

Conference Paper, Carnegie Mellon University, European Conference on Computer Vision (ECCV), September, 2010
Space-carving Kernels for Accurate Rough Terrain Estimation
Raia Hadsell, J. Andrew (Drew) Bagnell, Daniel Huber and Martial Hebert

Journal Article, Carnegie Mellon University, International Journal of Robotics Research, Vol. 29, No. 8, pp. 981-996, July, 2010
Modeling Interaction via the Principle of Maximum Causal Entropy
Brian D. Ziebart, J. Andrew (Drew) Bagnell and Anind Dey

Conference Paper, Carnegie Mellon University, International Conference on Machine Learning, June, 2010
Learning for Autonomous Navigation: Advances in Machine Learning for Rough Terrain Mobility
J. Andrew (Drew) Bagnell, David Bradley, David Silver, Boris Sofman and Anthony (Tony) Stentz

Magazine Article, Carnegie Mellon University, IEEE Robotics & Automation Magazine, Vol. 17, No. 2, pp. 74-84, June, 2010
Anytime Online Novelty Detection for Vehicle Safeguarding
Boris Sofman, J. Andrew (Drew) Bagnell and Anthony (Tony) Stentz

Conference Paper, Carnegie Mellon University, IEEE International Conference on Robotics and Automation, May, 2010
Efficient Reductions for Imitation Learning
Stephane Ross and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS), May, 2010
An Optimization Approach to Rough Terrain Locomotion
Matthew Zucker, J. Andrew (Drew) Bagnell, Chris Atkeson and James Kuffner

Conference Paper, Carnegie Mellon University, IEEE Conference on Robotics and Automation, May, 2010
Boosted Backpropagation Learning for Training Deep Modular Networks
Alexander Grubb and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, Proceedings of the 27th International Conference on Machine Learning, May, 2010
Reinforcement Planning: RL for Optimal Planners
Matthew Zucker and J. Andrew (Drew) Bagnell

Tech. Report, CMU-RI-TR-10-14, Robotics Institute, Carnegie Mellon University, April, 2010
On Two Methods for Semi-Supervised Structured Prediction
Daniel Munoz, J. Andrew (Drew) Bagnell and Martial Hebert

Tech. Report, CMU-RI-TR-10-02, Robotics Institute, Carnegie Mellon University, January, 2010
Domain Adaptation For Mobile Robot Navigation
David Bradley and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, January, 2010
Policy Gradient Methods
Jan Peters and J. Andrew (Drew) Bagnell

Book Section/Chapter, Springer Encyclopedia of Machine Learning, January, 2010
Planning-based Prediction for Pedestrians
Brian D. Ziebart, Nathan Ratliff, Garratt Gallagher, Christoph Mertz, Kevin Peterson, J. Andrew (Drew) Bagnell, Martial Hebert, Anind Dey and Siddhartha Srinivasa

Conference Paper, Carnegie Mellon University, Proc. IROS 2009, October, 2009
Perceptual Interpretation for Autonomous Navigation through Dynamic Imitation Learning
David Silver, J. Andrew (Drew) Bagnell and Anthony (Tony) Stentz

Conference Paper, Carnegie Mellon University, International Symposium of Robotics Research, August, 2009
Bandit-Based Online Candidate Selection for Adjustable Autonomy
Boris Sofman, J. Andrew (Drew) Bagnell and Anthony (Tony) Stentz

Conference Paper, Carnegie Mellon University, 7th International Conferences on Field and Service Robotics, July, 2009
Applied Imitation Learning for Autonomous Navigation in Complex Natural Terrain
David Silver, J. Andrew (Drew) Bagnell and Anthony (Tony) Stentz

Conference Paper, Carnegie Mellon University, Field and Service Robotics, July, 2009
Learning to search: Functional gradient techniques for imitation learning
Nathan Ratliff, David Silver and J. Andrew (Drew) Bagnell

Journal Article, Carnegie Mellon University, Autonomous Robots, Vol. 27, No. 1, pp. 25-53, July, 2009
Convex Coding
David Bradley and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, Uncertainty in Artificial Intelligence (UAI), June, 2009
Accurate Rough Terrain Estimation with Space-Carving Kernels
Raia Hadsell, J. Andrew (Drew) Bagnell, Daniel Huber and Martial Hebert

Conference Paper, Carnegie Mellon University, Proc. Robotics Science and Systems, June, 2009
Contextual Classification with Functional Max-Margin Markov Networks
Daniel Munoz, J. Andrew (Drew) Bagnell, Nicolas Vandapel and Martial Hebert

Conference Paper, Carnegie Mellon University, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June, 2009
CHOMP: Gradient Optimization Techniques for Efficient Motion Planning
Nathan Ratliff, Matthew Zucker, J. Andrew (Drew) Bagnell and Siddhartha Srinivasa

Conference Paper, Carnegie Mellon University, IEEE International Conference on Robotics and Automation (ICRA), May, 2009
GATMO: a Generalized Approach to Tracking Movable Objects
Garratt Gallagher, Siddhartha Srinivasa, J. Andrew (Drew) Bagnell and David Ferguson

Conference Paper, Carnegie Mellon University, IEEE International Conference on Robotics and Automation, May, 2009
Convex Coding
David Bradley and J. Andrew (Drew) Bagnell

Tech. Report, CMU-RI-TR-09-22, Robotics Institute, Carnegie Mellon University, May, 2009
Inverse Optimal Heuristic Control for Imitation Learning
Nathan Ratliff, Brian D. Ziebart, Kevin Peterson, J. Andrew (Drew) Bagnell, Martial Hebert, Anind Dey and Siddhartha Srinivasa

Conference Paper, Carnegie Mellon University, Twelfth International Conference on Artificial Intelligence and Statistics (AIStats), April, 2009
Anytime Online Novelty Detection for Vehicle Safeguarding
Boris Sofman, J. Andrew (Drew) Bagnell and Anthony (Tony) Stentz

Tech. Report, CMU-RI-TR-09-17, Robotics Institute, Carnegie Mellon University, April, 2009
A Space-Carving Approach to Surface Estimation
Santosh Kumar Divvala, J. Andrew (Drew) Bagnell and Martial Hebert

Conference Paper, Carnegie Mellon University, April, 2009
Differentiable Sparse Coding
David Bradley and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, Proceedings of Neural Information Processing Systems 22, December, 2008
Fast Planning for Dynamic Preferences
Brian D. Ziebart, Anind Dey and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, ICAPS: International Conference on Automated Planning and Scheduling, September, 2008
Maximum Entropy Inverse Reinforcement Learning
Brian D. Ziebart, Andrew Maas, J. Andrew (Drew) Bagnell and Anind Dey

Conference Paper, Carnegie Mellon University, Proceeding of AAAI 2008, July, 2008
High Performance Outdoor Navigation from Overhead Data using Imitation Learning
David Silver, J. Andrew (Drew) Bagnell and Anthony (Tony) Stentz

Conference Paper, Carnegie Mellon University, Robotics Science and Systems, June, 2008
Autonomous driving in urban environments: Boss and the Urban Challenge
Christopher Urmson, Joshua Anhalt, Hong Bae, J. Andrew (Drew) Bagnell, Christopher R. Baker, Robert E. Bittner, Thomas Brown, M. N. Clark, Michael Darms, Daniel Demitrish, John M. Dolan, David Duggins, David Ferguson, Tugrul Galatali, Christopher M. Geyer, Michele Gittleman, Sam Harbaugh, Martial Hebert, Thomas Howard, Sascha Kolski, Maxim Likhachev, Bakhtiar Litkouhi, Alonzo Kelly, Matthew McNaughton, Nick Miller, Jim Nickolaou, Kevin Peterson, Brian Pilnick, Raj Rajkumar, Paul Rybski, Varsha Sadekar, Bryan Salesky, Young-Woo Seo, Sanjiv Singh, Jarrod M. Snider, Joshua C. Struble, Anthony (Tony) Stentz, Michael Taylor, William (Red) L. Whittaker, Ziv Wolkowicki, Wende Zhang and Jason Ziglar

Journal Article, Carnegie Mellon University, Journal of Field Robotics Special Issue on the 2007 DARPA Urban Challenge, Part I, Vol. 25, No. 8, pp. 425-466, June, 2008
Adaptive Workspace Biasing for Sampling Based Planners
Matthew Zucker, James Kuffner and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, Proc. IEEE Int'l Conf. on Robotics and Automation, May, 2008
Adaptive workspace biasing for sampling-based planners
Matthew Zucker, James Kuffner and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, April, 2008
Human Behavior Modeling with Maximum Entropy Inverse Optimal Control
Brian D. Ziebart, Andrew L. Maas, J. Andrew (Drew) Bagnell and Anind Dey

Conference Paper, Carnegie Mellon University, April, 2008
Navigate Like a Cabbie: Probabilistic Reasoning from Observed Context-Aware Behavior
Brian D. Ziebart, Andrew Maas, Anind Dey and J. Andrew (Drew) Bagnell

Conference Paper, UBICOMP: Ubiquitious Computation, January, 2008
Imitation Learning for Locomotion and Manipulation
Nathan Ratliff, J. Andrew (Drew) Bagnell and Siddhartha Srinivasa

Tech. Report, CMU-RI-TR-07-45, Robotics Institute, Carnegie Mellon University, December, 2007
Imitation Learning for Locomotion and Manipulation
Nathan Ratliff, J. Andrew (Drew) Bagnell and Siddhartha Srinivasa

Conference Paper, Carnegie Mellon University, IEEE-RAS International Conference on Humanoid Robots, November, 2007
Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification
Brian D. Ziebart, Anind Dey and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, Proceedings of Uncertainty in Artificial Intelligence (UAI 2007), July, 2007
Vegetation Detection for Driving in Complex Environments
David Bradley, Ranjith Unnikrishnan and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, IEEE International Conference on Robotics and Automation, April, 2007
Tartan Racing: A Multi-Modal Approach to the DARPA Urban Challenge
Christopher Urmson, Joshua Anhalt, J. Andrew (Drew) Bagnell, Christopher R. Baker, Robert E. Bittner, John M. Dolan, David Duggins, David Ferguson, Tugrul Galatali, Hartmut Geyer, Michele Gittleman, Sam Harbaugh, Martial Hebert, Thomas Howard, Alonzo Kelly, David Kohanbash, Maxim Likhachev, Nick Miller, Kevin Peterson, Raj Rajkumar, Paul Rybski, Bryan Salesky, Sebastian Scherer, Young-Woo Seo, Reid Simmons, Sanjiv Singh, Jarrod M. Snider, Anthony (Tony) Stentz, William (Red) L. Whittaker and Jason Ziglar

Tech. Report, Robotics Institute, Carnegie Mellon University, DARPA Grand Challenge Tech Report, April, 2007
(Online) Subgradient Methods for Structured Prediction
Nathan Ratliff, J. Andrew (Drew) Bagnell and Martin Zinkevich

Conference Paper, Carnegie Mellon University, Eleventh International Conference on Artificial Intelligence and Statistics (AIStats), March, 2007
Boosting Structured Prediction for Imitation Learning
Nathan Ratliff, David Bradley, J. Andrew (Drew) Bagnell and Joel Chestnutt

Conference Paper, Advances in Neural Information Processing Systems 19, January, 2007
Kernel Conjugate Gradient for Fast Kernel Machines
Nathan Ratliff and J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, International Joint Conference on Artificial Intelligence, Vol. 20, January, 2007
Creating Low-Cost Soil Maps for Tropical Agriculture using Gaussian Processes
Juan Pablo Gonzalez, Simon Cook, Thomas Oberthur, Andrew Jarvis, J. Andrew (Drew) Bagnell and M Bernardine Dias

Conference Paper, Carnegie Mellon University, Workshop on AI in ICT for Development (ICTD) at the Twentieth International Joint Conference on Artificial Intelligence (IJCAI 2007), January, 2007
Improving Robot Navigation Through Self-Supervised Online Learning
Boris Sofman, Ellie Lin Ratliff, J. Andrew (Drew) Bagnell, John Cole, Nicolas Vandapel and Anthony (Tony) Stentz

Journal Article, Carnegie Mellon University, Journal of Field Robotics, Vol. 23, No. 12, December, 2006
Experimental Analysis of Overhead Data Processing To Support Long Range Navigation
David Silver, Boris Sofman, Nicolas Vandapel, J. Andrew (Drew) Bagnell and Anthony (Tony) Stentz

Conference Paper, Carnegie Mellon University, IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 2443 - 2450, October, 2006
Improving Robot Navigation Through Self-Supervised Online Learning
Boris Sofman, Ellie Lin Ratliff, J. Andrew (Drew) Bagnell, Nicolas Vandapel and Anthony (Tony) Stentz

Conference Paper, Carnegie Mellon University, Proceedings of Robotics: Science and Systems, August, 2006
Maximum Margin Planning
Nathan Ratliff, J. Andrew (Drew) Bagnell and Martin Zinkevich

Conference Paper, Carnegie Mellon University, International Conference on Machine Learning, July, 2006
On Local Rewards and Scaling Distributed Reinforcement Learning
J. Andrew (Drew) Bagnell and Andrew Ng

Conference Paper, Carnegie Mellon University, Neural Information Processing Systems, May, 2006
Subgradient Methods for Maximum Margin Structured Learning
Nathan Ratliff, J. Andrew (Drew) Bagnell and Martin Zinkevich

Conference Paper, Carnegie Mellon University, April, 2006
Terrain Classification from Aerial Data to Support Ground Vehicle Navigation
Boris Sofman, J. Andrew (Drew) Bagnell, Anthony (Tony) Stentz and Nicolas Vandapel

Tech. Report, CMU-RI-TR-05-39, Robotics Institute, Carnegie Mellon University, January, 2006
Gaussian Processes for Statistical Soil Modeling of the Tropics
Juan Pablo Gonzalez, J. Andrew (Drew) Bagnell, Simon Cook, Thomas Oberthur, Andrew Jarvis and Mauricio Rincon

Tech. Report, CMU-RI-TR-05-52, Robotics Institute, Carnegie Mellon University, October, 2005
Kernel Conjugate Gradient
Nathan Ratliff and J. Andrew (Drew) Bagnell

Tech. Report, CMU-RI-TR-05-30, Robotics Institute, Carnegie Mellon University, June, 2005
Cost-Sensitive Learning for Confidential Access Control
Young-Woo Seo, J. Andrew (Drew) Bagnell and Katia Sycara

Tech. Report, CMU-RI-TR-05-31, Robotics Institute, Carnegie Mellon University, June, 2005
Robust Supervised Learning
J. Andrew (Drew) Bagnell

Conference Paper, Carnegie Mellon University, Proceedings of AAAI, June, 2005
Learning Opportunity Costs in Multi-Robot Market Based Planners
Jeff Schneider, David Apfelbaum, J. Andrew (Drew) Bagnell and Reid Simmons

Conference Paper, Carnegie Mellon University, April, 2005
Learning Decisions: Robustness, Uncertainty, and Approximation
J. Andrew (Drew) Bagnell

PhD Thesis, CMU-RI-TR-04-67, Robotics Institute, Carnegie Mellon University, August, 2004
Policy Search by Dynamic Programming
J. Andrew (Drew) Bagnell, Sham Kakade, Andrew Ng and Jeff Schneider

Conference Paper, Carnegie Mellon University, Neural Information Processing Systems, Vol. 16, December, 2003
Policy Search in Reproducing Kernel Hilbert Space
J. Andrew (Drew) Bagnell and Jeff Schneider

Tech. Report, CMU-RI-TR-03-45, Robotics Institute, Carnegie Mellon University, November, 2003
Covariant Policy Search
J. Andrew (Drew) Bagnell and Jeff Schneider

Conference Paper, Carnegie Mellon University, Proceeding of the International Joint Conference on Artifical Intelligence, August, 2003
Learning with scope; with application to information extraction and classification
David Blei, J. Andrew (Drew) Bagnell and Andrew McCallum

Conference Paper, Carnegie Mellon University, Uncertainty in Artificial Intelligence, pp. 53-60, June, 2002
Learning with Scope, with Application to Information Extraction and Classification
David Blei, J. Andrew (Drew) Bagnell and Andrew Mccallum

Conference Paper, Carnegie Mellon University, April, 2002
Solving Uncertain Markov Decision Problems
J. Andrew (Drew) Bagnell, Andrew Y. Ng and Jeff Schneider

Tech. Report, CMU-RI-TR-01-25, Robotics Institute, Carnegie Mellon University, August, 2001
Autonomous Helicopter Control using Reinforcement Learning Policy Search Methods
J. Andrew (Drew) Bagnell and Jeff Schneider

Conference Paper, Carnegie Mellon University, Proceedings of the International Conference on Robotics and Automation 2001, May, 2001
Stabilizing Human Control Strategies through Reinforcement Learning
Michael Nechyba and J. Andrew (Drew) Bagnell

Conference Paper, Proc. IEEE Hong Kong Symp. on Robotics and Control, Vol. 1, pp. 39-44, April, 1999
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2017-09-13T10:48:58+00:00