J. Andrew Bagnell

According to our database, J. Andrew Bagnell authored at least 156 papers between 2001 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Hybrid Inverse Reinforcement Learning.

Juntao Ren, Gokul Swamy, Zhiwei Steven Wu, J. Andrew Bagnell, Sanjiban Choudhury

CoRR, 2024

2023

Inverse Reinforcement Learning without Reinforcement Learning.

Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu

CoRR, 2023

The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms.

Anirudh Vemula, Yuda Song, Aarti Singh, Drew Bagnell, Sanjiban Choudhury

Proceedings of the International Conference on Machine Learning, 2023

Inverse Reinforcement Learning without Reinforcement Learning.

Gokul Swamy, David Wu, Sanjiban Choudhury, Drew Bagnell, Zhiwei Steven Wu

Proceedings of the International Conference on Machine Learning, 2023

Hybrid RL: Using both offline and online data can make RL efficient.

Yuda Song, Yifei Zhou, Ayush Sekhari, Drew Bagnell, Akshay Krishnamurthy, Wen Sun

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Game-Theoretic Algorithms for Conditional Moment Matching.

Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu

CoRR, 2022

Minimax Optimal Online Imitation Learning via Replay Estimation.

Gokul Swamy, Nived Rajaraman, Matthew Peng, Sanjiban Choudhury, J. Andrew Bagnell, Steven Wu, Jiantao Jiao, Kannan Ramchandran

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Sequence Model Imitation Learning with Unobserved Contexts.

Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On the Effectiveness of Iterative Learning Control.

Anirudh Vemula, Wen Sun, Maxim Likhachev, J. Andrew Bagnell

Proceedings of the Learning for Dynamics and Control Conference, 2022

Causal Imitation Learning under Temporally Correlated Noise.

Gokul Swamy, Sanjiban Choudhury, Drew Bagnell, Steven Wu

Proceedings of the International Conference on Machine Learning, 2022

2021

A Critique of Strictly Batch Imitation Learning.

Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu

CoRR, 2021

Of Moments and Matching: Trade-offs and Treatments in Imitation Learning.

Gokul Swamy, Sanjiban Choudhury, Zhiwei Steven Wu, J. Andrew Bagnell

CoRR, 2021

Feedback in Imitation Learning: The Three Regimes of Covariate Shift.

Jonathan C. Spencer, Sanjiban Choudhury, Arun Venkatraman, Brian D. Ziebart, J. Andrew Bagnell

CoRR, 2021

Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap.

Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Steven Wu

Proceedings of the 38th International Conference on Machine Learning, 2021

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

Anirudh Vemula, J. Andrew Bagnell, Maxim Likhachev

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Exploration in Action Space.

Anirudh Vemula, Wen Sun, J. Andrew Bagnell

CoRR, 2020

Planning and Execution using Inaccurate Models with Provable Guarantees.

Anirudh Vemula, Yash Oza, J. Andrew Bagnell, Maxim Likhachev

Proceedings of the Robotics: Science and Systems XVI, 2020

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

Anirudh Vemula, J. Andrew Bagnell

Proceedings of the 59th IEEE Conference on Decision and Control, 2020

2019

Provably Efficient Imitation Learning from Observation Alone.

Wen Sun, Anirudh Vemula, Byron Boots, Drew Bagnell

Proceedings of the 36th International Conference on Machine Learning, 2019

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

Anirudh Vemula, Wen Sun, J. Andrew Bagnell

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing.

Hanzhang Hu, Debadeepta Dey, Martial Hebert, J. Andrew Bagnell

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

A convex polynomial model for planar sliding mechanics: theory, application, and experimental validation.

Jiaji Zhou, Matthew T. Mason, Robert Paolini, Drew Bagnell

Int. J. Robotics Res., 2018

Shared autonomy via hindsight optimization for teleoperation and teaming.

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

Int. J. Robotics Res., 2018

An Algorithmic Perspective on Imitation Learning.

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

Found. Trends Robotics, 2018

Dual Policy Iteration.

Wen Sun, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Truncated horizon Policy Search: Combining Reinforcement Learning & Imitation Learning.

Wen Sun, J. Andrew Bagnell, Byron Boots

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Policy Gradient Methods.

Jan Peters, J. Andrew Bagnell

Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

A Probabilistic Planning Framework for Planar Grasping Under Uncertainty.

Jiaji Zhou, Robert Paolini, Aaron M. Johnson, J. Andrew Bagnell, Matthew T. Mason

IEEE Robotics Autom. Lett., 2017

Log-DenseNet: How to Sparsify a DenseNet.

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

CoRR, 2017

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

Allison Del Giorno, J. Andrew Bagnell, Martial Hebert

CoRR, 2017

Anytime Neural Networks via Joint Optimization of Auxiliary Losses.

Hanzhang Hu, Debadeepta Dey, J. Andrew Bagnell, Martial Hebert

CoRR, 2017

Autonomy infused teleoperation with application to brain computer interface controlled manipulation.

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

Auton. Robots, 2017

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

Jiaji Zhou, James A. Bagnell, Matthew T. Mason

Proceedings of the Robotics: Science and Systems XIII, 2017

Predictive-State Decoders: Encoding the Future into Recurrent Networks.

Arun Venkatraman, Nicholas Rhinehart, Wen Sun, Lerrel Pinto, Martial Hebert, Byron Boots, Kris M. Kitani, James Andrew Bagnell

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction.

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

Proceedings of the 34th International Conference on Machine Learning, 2017

Gradient Boosting on Stochastic Data Streams.

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

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Robot Learning.

Jan Peters, Daniel D. Lee, Jens Kober, Duy Nguyen-Tuong, J. Andrew Bagnell, Stefan Schaal

Proceedings of the Springer Handbook of Robotics, 2016

Robust Monocular Flight in Cluttered Outdoor Environments.

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

CoRR, 2016

Learning to Smooth with Bidirectional Predictive State Inference Machines.

Wen Sun, Roberto Capobianco, Geoffrey J. Gordon, J. Andrew Bagnell, Byron Boots

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

Efficient Feature Group Sequencing for Anytime Linear Prediction.

Hanzhang Hu, Alexander Grubb, J. Andrew Bagnell, Martial Hebert

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

Improved Learning of Dynamics Models for Control.

Arun Venkatraman, Roberto Capobianco, Lerrel Pinto, Martial Hebert, Daniele Nardi, J. Andrew Bagnell

Proceedings of the International Symposium on Experimental Robotics, 2016

Learning Transferable Policies for Monocular Reactive MAV Control.

Shreyansh Daftry, J. Andrew Bagnell, Martial Hebert

Proceedings of the International Symposium on Experimental Robotics, 2016

Introspective perception: Learning to predict failures in vision systems.

Shreyansh Daftry, Sam Zeng, J. Andrew Bagnell, Martial Hebert

Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016

Inference Machines for Nonparametric Filter Learning.

Arun Venkatraman, Wen Sun, Martial Hebert, Byron Boots, J. Andrew Bagnell

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

Online Bellman Residual and Temporal Difference Algorithms with Predictive Error Guarantees.

Wen Sun, J. Andrew Bagnell

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

A convex polynomial force-motion model for planar sliding: Identification and application.

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

Proceedings of the 2016 IEEE International Conference on Robotics and Automation, 2016

Learning to Filter with Predictive State Inference Machines.

Wen Sun, Arun Venkatraman, Byron Boots, J. Andrew Bagnell

Proceedings of the 33nd International Conference on Machine Learning, 2016

Minimizing User Cost for Shared Autonomy.

Shervin Javdani, James Andrew Bagnell, Siddhartha S. Srinivasa

Proceedings of the Eleventh ACM/IEEE International Conference on Human Robot Interation, 2016

A Discriminative Framework for Anomaly Detection in Large Videos.

Allison Del Giorno, J. Andrew Bagnell, Martial Hebert

Proceedings of the Computer Vision - ECCV 2016, 2016

Online Instrumental Variable Regression with Applications to Online Linear System Identification.

Arun Venkatraman, Wen Sun, Martial Hebert, J. Andrew Bagnell, Byron Boots

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

CHIMP, the CMU Highly Intelligent Mobile Platform.

Anthony Stentz, Herman Herman, Alonzo Kelly, Eric Meyhofer, G. Clark Haynes, David Stager, Brian Zajac, J. Andrew Bagnell, Jordan Brindza, Christopher M. Dellin, Michael David George, Jose Gonzalez-Mora, Sean Hyde, Morgan Jones, Michel Laverne, Maxim Likhachev, Levi Lister, Matthew Powers, Oscar E. Ramos, Justin Ray, David Rice, Justin Scheifflee, Raumi Sidki, Siddhartha S. Srinivasa, Kyle Strabala, Jean-Philippe Tardif, Jean-Sebastien Valois, Michael Vande Weghe, Michael Wagner, Carl Wellington

J. Field Robotics, 2015

Online Bellman Residual Algorithms with Predictive Error Guarantees.

Wen Sun, J. Andrew Bagnell

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Autonomy Infused Teleoperation with Application to BCI Manipulation.

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

Proceedings of the Robotics: Science and Systems XI, Sapienza University of Rome, 2015

Shared Autonomy via Hindsight Optimization.

Shervin Javdani, Siddhartha S. Srinivasa, J. Andrew Bagnell

Proceedings of the Robotics: Science and Systems XI, Sapienza University of Rome, 2015

Theoretical Limits of Speed and Resolution for Kinodynamic Planning in a Poisson Forest.

Sanjiban Choudhury, Sebastian A. Scherer, J. Andrew Bagnell

Proceedings of the Robotics: Science and Systems XI, Sapienza University of Rome, 2015

Visual chunking: A list prediction framework for region-based object detection.

Nicholas Rhinehart, Jiaji Zhou, Martial Hebert, J. Andrew Bagnell

Proceedings of the IEEE International Conference on Robotics and Automation, 2015

Movement primitives via optimization.

Anca D. Dragan, Katharina Mülling, J. Andrew Bagnell, Siddhartha S. Srinivasa

Proceedings of the IEEE International Conference on Robotics and Automation, 2015

Predicting Multiple Structured Visual Interpretations.

Debadeepta Dey, Varun Ramakrishna, Martial Hebert, J. Andrew Bagnell

Proceedings of the 2015 IEEE International Conference on Computer Vision, 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, J. Andrew Bagnell

Proceedings of the Field and Service Robotics, 2015

Solving Games with Functional Regret Estimation.

Kevin Waugh, Dustin Morrill, James Andrew Bagnell, Michael Bowling

Proceedings of the Computer Poker and Imperfect Information, 2015

A Unified View of Large-Scale Zero-Sum Equilibrium Computation.

Kevin Waugh, James Andrew Bagnell

Proceedings of the Computer Poker and Imperfect Information, 2015

Improving Multi-Step Prediction of Learned Time Series Models.

Arun Venkatraman, Martial Hebert, J. Andrew Bagnell

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

Approximate MaxEnt Inverse Optimal Control and Its Application for Mental Simulation of Human Interactions.

De-An Huang, Amir-massoud Farahmand, Kris M. Kitani, James Andrew Bagnell

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

Submodular Surrogates for Value of Information.

Yuxin Chen, Shervin Javdani, Amin Karbasi, J. Andrew Bagnell, Siddhartha S. Srinivasa, Andreas Krause

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

Learning to Manipulate Unknown Objects in Clutter by Reinforcement.

Abdeslam Boularias, James Andrew Bagnell, Anthony Stentz

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014

Reinforcement and Imitation Learning via Interactive No-Regret Learning.

Stéphane Ross, J. Andrew Bagnell

CoRR, 2014

Human-inspired force compliant grasping primitives.

Moslem Kazemi, Jean-Sebastien Valois, J. Andrew Bagnell, Nancy S. Pollard

Auton. Robots, 2014

Perceiving, learning, and exploiting object affordances for autonomous pile manipulation.

Dov Katz, Arun Venkatraman, Moslem Kazemi, J. Andrew Bagnell, Anthony Stentz

Auton. Robots, 2014

Pose Machines: Articulated Pose Estimation via Inference Machines.

Varun Ramakrishna, Daniel Munoz, Martial Hebert, James Andrew Bagnell, Yaser Sheikh

Proceedings of the Computer Vision - ECCV 2014, 2014

Near Optimal Bayesian Active Learning for Decision Making.

Shervin Javdani, Yuxin Chen, Amin Karbasi, Andreas Krause, Drew Bagnell, Siddhartha S. Srinivasa

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

Efficient Optimization for Autonomous Robotic Manipulation of Natural Objects.

Abdeslam Boularias, James Andrew Bagnell, Anthony Stentz

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

2013

The Principle of Maximum Causal Entropy for Estimating Interacting Processes.

Brian D. Ziebart, J. Andrew Bagnell, Anind K. Dey

IEEE Trans. Inf. Theory, 2013

CHOMP: Covariant Hamiltonian optimization for motion planning.

Matthew Zucker, Nathan D. Ratliff, Anca D. Dragan, Mihail Pivtoraiko, Matthew Klingensmith, Christopher M. Dellin, J. Andrew Bagnell, Siddhartha S. Srinivasa

Int. J. Robotics Res., 2013

Reinforcement learning in robotics: A survey.

Jens Kober, J. Andrew Bagnell, Jan Peters

Int. J. Robotics Res., 2013

Knapsack Constrained Contextual Submodular List Prediction with Application to Multi-document Summarization.

Jiaji Zhou, Stéphane Ross, Yisong Yue, Debadeepta Dey, J. Andrew Bagnell

CoRR, 2013

SpeedMachines: Anytime Structured Prediction.

Alexander Grubb, Daniel Munoz, J. Andrew Bagnell, Martial Hebert

CoRR, 2013

Learning monocular reactive UAV control in cluttered natural environments.

Stéphane Ross, Narek Melik-Barkhudarov, Kumar Shaurya Shankar, Andreas Wendel, Debadeepta Dey, J. Andrew Bagnell, Martial Hebert

Proceedings of the 2013 IEEE International Conference on Robotics and Automation, 2013

Efficient temporal consistency for streaming video scene analysis.

Ondrej Miksik, Daniel Munoz, J. Andrew Bagnell, Martial Hebert

Proceedings of the 2013 IEEE International Conference on Robotics and Automation, 2013

Interactive segmentation, tracking, and kinematic modeling of unknown 3D articulated objects.

Dov Katz, Moslem Kazemi, J. Andrew Bagnell, Anthony Stentz

Proceedings of the 2013 IEEE International Conference on Robotics and Automation, 2013

Clearing a pile of unknown objects using interactive perception.

Dov Katz, Moslem Kazemi, J. Andrew Bagnell, Anthony Stentz

Proceedings of the 2013 IEEE International Conference on Robotics and Automation, 2013

Efficient touch based localization through submodularity.

Shervin Javdani, Matthew Klingensmith, J. Andrew Bagnell, Nancy S. Pollard, Siddhartha S. Srinivasa

Proceedings of the 2013 IEEE International Conference on Robotics and Automation, 2013

Efficient 3-D scene analysis from streaming data.

Hanzhang Hu, Daniel Munoz, J. Andrew Bagnell, Martial Hebert

Proceedings of the 2013 IEEE International Conference on Robotics and Automation, 2013

Learning Policies for Contextual Submodular Prediction.

Stéphane Ross, Jiaji Zhou, Yisong Yue, Debadeepta Dey, Drew Bagnell

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

SpeedBoost: Anytime Prediction with Uniform Near-Optimality.

Alexander Grubb, Drew Bagnell

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Predicting Contextual Sequences via Submodular Function Maximization

Debadeepta Dey, Tian Yu Liu, Martial Hebert, J. Andrew Bagnell

CoRR, 2012

Robust Object Grasping using Force Compliant Motion Primitives.

Moslem Kazemi, Jean-Sebastien Valois, J. Andrew Bagnell, Nancy S. Pollard

Proceedings of the Robotics: Science and Systems VIII, 2012

Contextual Sequence Prediction with Application to Control Library Optimization.

Debadeepta Dey, Tian Yu Liu, Martial Hebert, J. Andrew Bagnell

Proceedings of the Robotics: Science and Systems VIII, 2012

Efficient high dimensional maximum entropy modeling via symmetric partition functions.

Paul Vernaza, Drew Bagnell

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Probabilistic pointing target prediction via inverse optimal control.

Brian D. Ziebart, Anind K. Dey, J. Andrew Bagnell

Proceedings of the 17th International Conference on Intelligent User Interfaces, 2012

Learning Autonomous Driving Styles and Maneuvers from Expert Demonstration.

David Silver, J. Andrew Bagnell, Anthony Stentz

Proceedings of the Experimental Robotics, 2012

An integrated system for autonomous robotics manipulation.

J. Andrew Bagnell, Felipe Cavalcanti, Lei Cui, Thomas Galluzzo, Martial Hebert, Moslem Kazemi, Matthew Klingensmith, Jacqueline Libby, Tian Yu Liu, Nancy S. Pollard, Mihail Pivtoraiko, Jean-Sebastien Valois, Ranqi Zhu

Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012

Reinforcement Planning: RL for optimal planners.

Matthew Zucker, J. Andrew Bagnell

Proceedings of the IEEE International Conference on Robotics and Automation, 2012

Active learning from demonstration for robust autonomous navigation.

David Silver, J. Andrew Bagnell, Anthony Stentz

Proceedings of the IEEE International Conference on Robotics and Automation, 2012

Agnostic System Identification for Model-Based Reinforcement Learning.

Stéphane Ross, Drew Bagnell

Proceedings of the 29th International Conference on Machine Learning, 2012

Co-inference for Multi-modal Scene Analysis.

Daniel Munoz, James Andrew Bagnell, Martial Hebert

Proceedings of the Computer Vision - ECCV 2012, 2012

Activity Forecasting.

Kris M. Kitani, Brian D. Ziebart, James Andrew Bagnell, Martial Hebert

Proceedings of the Computer Vision - ECCV 2012, 2012

Detecting Interesting Events Using Unsupervised Density Ratio Estimation.

Yuichi Ito, Kris M. Kitani, James A. Bagnell, Martial Hebert

Proceedings of the Computer Vision - ECCV 2012. Workshops and Demonstrations, 2012

Efficient Optimization of Control Libraries.

Debadeepta Dey, Tian Yu Liu, Boris Sofman, James Andrew Bagnell

Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012

2011

A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning.

Stéphane Ross, Geoffrey J. Gordon, Drew Bagnell

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Anytime online novelty and change detection for mobile robots.

Boris Sofman, Bradford Neuman, Anthony Stentz, J. Andrew Bagnell

J. Field Robotics, 2011

Optimization and learning for rough terrain legged locomotion.

Matthew Zucker, Nathan D. Ratliff, Martin Stolle, Joel E. Chestnutt, J. Andrew Bagnell, Christopher G. Atkeson, James Kuffner

Int. J. Robotics Res., 2011

Stability Conditions for Online Learnability

Stéphane Ross, J. Andrew Bagnell

CoRR, 2011

3-D scene analysis via sequenced predictions over points and regions.

Xuehan Xiong, Daniel Munoz, J. Andrew Bagnell, Martial Hebert

Proceedings of the IEEE International Conference on Robotics and Automation, 2011

Segmentation-based online change detection for mobile robots.

Bradford Neuman, Boris Sofman, Anthony Stentz, J. Andrew Bagnell

Proceedings of the IEEE International Conference on Robotics and Automation, 2011

Computational Rationalization: The Inverse Equilibrium Problem.

Kevin Waugh, Brian D. Ziebart, Drew Bagnell

Proceedings of the 28th International Conference on Machine Learning, 2011

Generalized Boosting Algorithms for Convex Optimization.

Alexander Grubb, Drew Bagnell

Proceedings of the 28th International Conference on Machine Learning, 2011

Learning message-passing inference machines for structured prediction.

Stéphane Ross, Daniel Munoz, Martial Hebert, J. Andrew Bagnell

Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition, 2011

2010

Policy Gradient Methods.

Jan Peters, J. Andrew Bagnell

Proceedings of the Encyclopedia of Machine Learning, 2010

Learning for Autonomous Navigation.

J. Andrew Bagnell, David M. Bradley, David Silver, Boris Sofman, Anthony Stentz

IEEE Robotics Autom. Mag., 2010

Efficient Reductions for Imitation Learning.

Stéphane Ross, Drew Bagnell

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain.

David Silver, J. Andrew Bagnell, Anthony Stentz

Int. J. Robotics Res., 2010

Space-carving Kernels for Accurate Rough Terrain Estimation.

Raia Hadsell, J. Andrew Bagnell, Daniel F. Huber, Martial Hebert

Int. J. Robotics Res., 2010

No-Regret Reductions for Imitation Learning and Structured Prediction

Stéphane Ross, Geoffrey J. Gordon, J. Andrew Bagnell

CoRR, 2010

An optimization approach to rough terrain locomotion.

Matthew Zucker, James A. Bagnell, Christopher G. Atkeson, James Kuffner

Proceedings of the IEEE International Conference on Robotics and Automation, 2010

Anytime online novelty detection for vehicle safeguarding.

Boris Sofman, James A. Bagnell, Anthony Stentz

Proceedings of the IEEE International Conference on Robotics and Automation, 2010

Modeling Interaction via the Principle of Maximum Causal Entropy.

Brian D. Ziebart, J. Andrew Bagnell, Anind K. Dey

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

Boosted Backpropagation Learning for Training Deep Modular Networks.

Alexander Grubb, J. Andrew Bagnell

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

Stacked Hierarchical Labeling.

Daniel Munoz, J. Andrew Bagnell, Martial Hebert

Proceedings of the Computer Vision - ECCV 2010, 2010

Maximum Causal Entropy Correlated Equilibria for Markov Games.

Brian D. Ziebart, Drew Bagnell, Anind K. Dey

Proceedings of the Interactive Decision Theory and Game Theory, 2010

2009

Inverse Optimal Heuristic Control for Imitation Learning.

Nathan D. Ratliff, Brian D. Ziebart, Kevin M. Peterson, J. Andrew Bagnell, Martial Hebert, Anind K. Dey, Siddhartha S. Srinivasa

Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

Learning to search: Functional gradient techniques for imitation learning.

Nathan D. Ratliff, David Silver, J. Andrew Bagnell

Auton. Robots, 2009

Convex Coding.

David M. Bradley, J. Andrew Bagnell

Proceedings of the UAI 2009, 2009

Accurate rough terrain estimation with space-carving kernels.

Raia Hadsell, J. Andrew Bagnell, Daniel F. Huber, Martial Hebert

Proceedings of the Robotics: Science and Systems V, University of Washington, Seattle, USA, June 28, 2009

Perceptual Interpretation for Autonomous Navigation through Dynamic Imitation Learning.

David Silver, J. Andrew Bagnell, Anthony Stentz

Proceedings of the Robotics Research - The 14th International Symposium, 2009

Planning-based prediction for pedestrians.

Brian D. Ziebart, Nathan D. Ratliff, Garratt Gallagher, Christoph Mertz, Kevin M. Peterson, James A. Bagnell, Martial Hebert, Anind K. Dey, Siddhartha S. Srinivasa

Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009

CHOMP: Gradient optimization techniques for efficient motion planning.

Nathan D. Ratliff, Matthew Zucker, J. Andrew Bagnell, Siddhartha S. Srinivasa

Proceedings of the 2009 IEEE International Conference on Robotics and Automation, 2009

GATMO: A Generalized Approach to Tracking Movable Objects.

Garratt Gallagher, Siddhartha S. Srinivasa, J. Andrew Bagnell, Dave Ferguson

Proceedings of the 2009 IEEE International Conference on Robotics and Automation, 2009

Bandit-Based Online Candidate Selection for Adjustable Autonomy.

Boris Sofman, J. Andrew Bagnell, Anthony Stentz

Proceedings of the Field and Service Robotics, Results of the 7th International Conference, 2009

Applied Imitation Learning for Autonomous Navigation in Complex Natural Terrain.

David Silver, J. Andrew Bagnell, Anthony Stentz

Proceedings of the Field and Service Robotics, Results of the 7th International Conference, 2009

Contextual classification with functional Max-Margin Markov Networks.

Daniel Munoz, James A. Bagnell, Nicolas Vandapel, Martial Hebert

Proceedings of the 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 2009

Human Behavior Modeling with Maximum Entropy Inverse Optimal Control.

Brian D. Ziebart, Andrew L. Maas, J. Andrew Bagnell, Anind K. Dey

Proceedings of the Human Behavior Modeling, 2009

2008

Autonomous driving in urban environments: Boss and the Urban Challenge.

Chris Urmson, Joshua Anhalt, Drew Bagnell, Christopher R. Baker, Robert Bittner, M. N. Clark, John M. Dolan, Dave Duggins, Tugrul Galatali, Christopher Geyer, Michele Gittleman, Sam Harbaugh, Martial Hebert, Thomas M. Howard, Sascha Kolski, Alonzo Kelly, Maxim Likhachev, Matthew McNaughton, Nick Miller, Kevin M. Peterson, Brian Pilnick, Raj Rajkumar, Paul E. Rybski, Bryan Salesky, Young-Woo Seo, Sanjiv Singh, Jarrod M. Snider, Anthony Stentz, William Whittaker, Ziv Wolkowicki, Jason Ziglar, Hong Bae, Thomas Brown, Daniel Demitrish, Bakhtiar Litkouhi, Jim Nickolaou, Varsha Sadekar, Wende Zhang, Joshua Struble, Michael Taylor, Michael Darms, Dave Ferguson

J. Field Robotics, 2008

Editorial: Special Issue on Machine Learning in Robotics.

J. Andrew Bagnell, Stefan Schaal

Int. J. Robotics Res., 2008

High Performance Outdoor Navigation from Overhead Data using Imitation Learning.

David Silver, James A. Bagnell, Anthony Stentz

Proceedings of the Robotics: Science and Systems IV, 2008

Differentiable Sparse Coding.

J. Andrew Bagnell, David M. Bradley

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Adaptive workspace biasing for sampling-based planners.

Matthew Zucker, James Kuffner, James A. Bagnell

Proceedings of the 2008 IEEE International Conference on Robotics and Automation, 2008

Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior.

Brian D. Ziebart, Andrew L. Maas, Anind K. Dey, J. Andrew Bagnell

Proceedings of the UbiComp 2008: Ubiquitous Computing, 10th International Conference, 2008

Fast Planning for Dynamic Preferences.

Brian D. Ziebart, Anind K. Dey, J. Andrew Bagnell

Proceedings of the Eighteenth International Conference on Automated Planning and Scheduling, 2008

Maximum Entropy Inverse Reinforcement Learning.

Brian D. Ziebart, Andrew L. Maas, J. Andrew Bagnell, Anind K. Dey

Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, 2008

2007

(Approximate) Subgradient Methods for Structured Prediction.

Nathan D. Ratliff, J. Andrew Bagnell, Martin Zinkevich

Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification.

Brian D. Ziebart, Anind K. Dey, James A. Bagnell

Proceedings of the UAI 2007, 2007

Kernel Conjugate Gradient for Fast Kernel Machines.

Nathan D. Ratliff, J. Andrew Bagnell

Proceedings of the IJCAI 2007, 2007

Vegetation Detection for Driving in Complex Environments.

David M. Bradley, Ranjith Unnikrishnan, J. Andrew Bagnell

Proceedings of the 2007 IEEE International Conference on Robotics and Automation, 2007

Imitation learning for locomotion and manipulation.

Nathan D. Ratliff, James A. Bagnell, Siddhartha S. Srinivasa

Proceedings of the 2007 7th IEEE-RAS International Conference on Humanoid Robots, November 29th, 2007

2006

Improving robot navigation through self-supervised online learning.

Boris Sofman, Ellie Lin, J. Andrew Bagnell, John Cole, Nicolas Vandapel, Anthony Stentz

J. Field Robotics, 2006

Improving Robot Navigation Through Self-Supervised Online Learning.

Boris Sofman, Ellie Lin, J. Andrew Bagnell, Nicolas Vandapel, Anthony Stentz

Proceedings of the Robotics: Science and Systems II, 2006

Boosting Structured Prediction for Imitation Learning.

Nathan D. Ratliff, David M. Bradley, J. Andrew Bagnell, Joel E. Chestnutt

Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Experimental Analysis of Overhead Data Processing To Support Long Range Navigation.

David Silver, Boris Sofman, Nicolas Vandapel, J. Andrew Bagnell, Anthony Stentz

Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2006

Maximum margin planning.

Nathan D. Ratliff, J. Andrew Bagnell, Martin Zinkevich

Proceedings of the Machine Learning, 2006

2005

On Local Rewards and Scaling Distributed Reinforcement Learning.

J. Andrew Bagnell, Andrew Y. Ng

Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Learning Opportunity Costs in Multi-Robot Market Based Planners.

Jeff G. Schneider, David Apfelbaum, Drew Bagnell, Reid G. Simmons

Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005

Robust Supervised Learning.

J. Andrew Bagnell

Proceedings of the Proceedings, 2005

2003

Policy Search by Dynamic Programming.

J. Andrew Bagnell, Sham M. Kakade, Andrew Y. Ng, Jeff G. Schneider

Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Covariant Policy Search.

J. Andrew Bagnell, Jeff G. Schneider

Proceedings of the IJCAI-03, 2003

2002

Learning with Scope, with Application to Information Extraction and Classification.

David M. Blei, J. Andrew Bagnell, Andrew McCallum

Proceedings of the UAI '02, 2002

2001

Autonomous Helicopter Control using Reinforcement Learning Policy Search Methods.

J. Andrew Bagnell, Jeff G. Schneider

Proceedings of the 2001 IEEE International Conference on Robotics and Automation, 2001