J. Andrew Bagnell

发表

Learning to Manipulate Unknown Objects in Clutter by Reinforcement

Abdeslam Boularias, J. Andrew Bagnell, Anthony Stentz, 2015, AAAI.

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

J. Andrew Bagnell, Matthew T. Mason, Jiaji Zhou, 2018, Int. J. Robotics Res..

Siddhartha S. Srinivasa, Anca D. Dragan, Katharina Mülling, 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

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

Yisong Yue, J. Andrew Bagnell, Debadeepta Dey, 2013, ArXiv.

Terrain Classification from Aerial Data to Support Ground Vehicle Navigation

J. Andrew Bagnell, Anthony Stentz, Nicolas Vandapel, 2006 .

Learning monocular reactive UAV control in cluttered natural environments

Martial Hebert, Andreas Wendel, J. Andrew Bagnell, 2012, 2013 IEEE International Conference on Robotics and Automation.

Detecting Interesting Events Using Unsupervised Density Ratio Estimation

Martial Hebert, Yuichi Ito, J. Andrew Bagnell, 2012, ECCV Workshops.

Solving Games with Functional Regret Estimation

Kevin Waugh, Michael H. Bowling, J. Andrew Bagnell, 2014, AAAI Workshop: Computer Poker and Imperfect Information.

Improved Learning of Dynamics Models for Control

Martial Hebert, Daniele Nardi, J. Andrew Bagnell, 2016, ISER.

Planning and Execution using Inaccurate Models with Provable Guarantees pdf

J. Andrew Bagnell, Maxim Likhachev, Anirudh Vemula, 2020, Robotics: Science and Systems.

Experimental Analysis of Overhead Data Processing To Support Long Range Navigation

David Silver, J. Andrew Bagnell, Anthony Stentz, 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Maximum Causal Entropy Correlated Equilibria for Markov Games

Anind K. Dey, J. Andrew Bagnell, Brian D. Ziebart, 2011, Interactive Decision Theory and Game Theory.

Segmentation-based online change detection for mobile robots

J. Andrew Bagnell, Anthony Stentz, Boris Sofman, 2011, 2011 IEEE International Conference on Robotics and Automation.

Robust Object Grasping using Force Compliant Motion Primitives

J. Andrew Bagnell, Moslem Kazemi, Nancy S. Pollard, 2012, Robotics: Science and Systems.

Contextual classification with functional Max-Margin Markov Networks

Martial Hebert, J. Andrew Bagnell, Nicolas Vandapel, 2009, CVPR.

Robust Supervised Learning

J. Andrew Bagnell, J. Bagnell, 2005, AAAI.

Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction

Byron Boots, Geoffrey J. Gordon, J. Andrew Bagnell, 2017, ICML.

SpeedBoost: Anytime Prediction with Uniform Near-Optimality

J. Andrew Bagnell, Alexander Grubb, 2012, AISTATS.

Maximum Entropy Inverse Reinforcement Learning

Anind K. Dey, J. Andrew Bagnell, Brian D. Ziebart, 2008, AAAI.

Vegetation Detection for Driving in Complex Environments

David M. Bradley, J. Andrew Bagnell, Ranjith Unnikrishnan, 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

Boosted Backpropagation Learning for Training Deep Modular Networks

J. Andrew Bagnell, Alexander Grubb, 2010, ICML.

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

J. Andrew Bagnell, Anirudh Vemula, J. Bagnell, 2020, 2020 59th IEEE Conference on Decision and Control (CDC).

Policy Search by Dynamic Programming

Jeff G. Schneider, Sham M. Kakade, Andrew Y. Ng, 2003, NIPS.

Learning to Smooth with Bidirectional Predictive State Inference Machines

Byron Boots, Geoffrey J. Gordon, J. Andrew Bagnell, 2016, UAI.

Vision and Learning for Deliberative Monocular Cluttered Flight pdf

Martial Hebert, J. Andrew Bagnell, Debadeepta Dey, 2014, FSR.

Activity Forecasting

Martial Hebert, J. Andrew Bagnell, Kris M. Kitani, 2012, ECCV.

Contextual Sequence Prediction with Application to Control Library Optimization

Martial Hebert, J. Andrew Bagnell, Debadeepta Dey, 2012, Robotics: Science and Systems.

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

Kevin Waugh, J. Andrew Bagnell, J. Bagnell, 2014, AAAI Workshop: Computer Poker and Imperfect Information.

Anytime online novelty and change detection for mobile robots

J. Andrew Bagnell, Anthony Stentz, Boris Sofman, 2011, J. Field Robotics.

Efficient Optimization of Control Libraries

J. Andrew Bagnell, Debadeepta Dey, Boris Sofman, 2011, AAAI.

Shared Autonomy via Hindsight Optimization

Siddhartha S. Srinivasa, J. Andrew Bagnell, Shervin Javdani, 2015, Robotics: Science and Systems.

The Principle of Maximum Causal Entropy for Estimating Interacting Processes

Anind K. Dey, J. Andrew Bagnell, Brian D. Ziebart, 2013, IEEE Transactions on Information Theory.

Inverse Optimal Heuristic Control for Imitation Learning

Siddhartha S. Srinivasa, Martial Hebert, Anind K. Dey, 2009, AISTATS.

SpeedMachines: Anytime Structured Prediction pdf

Martial Hebert, J. Andrew Bagnell, Daniel Munoz, 2013, ArXiv.

Autonomy Infused Teleoperation with Application to BCI Manipulation pdf

Martial Hebert, Jeffrey Weiss, Katharina Mülling, 2015, Robotics: Science and Systems.

Modeling Interaction via the Principle of Maximum Causal Entropy

Anind K. Dey, J. Andrew Bagnell, Brian D. Ziebart, 2010, ICML.

Learning to Filter with Predictive State Inference Machines pdf

Byron Boots, J. Andrew Bagnell, Arun Venkatraman, 2015, ICML.

Inference Machines for Nonparametric Filter Learning

Byron Boots, Martial Hebert, J. Andrew Bagnell, 2016, IJCAI.

Online) Subgradient Methods for Structured Prediction

Nathan Ratliff, J. Andrew Bagnell, Nathan D. Ratliff, 2007 .

Semi-Dense Visual Odometry for Monocular Navigation in Clutt ered Environment

Martial Hebert, Harsimrat Sandhawalia, J. Andrew Bagnell, 2015 .

Space-carving Kernels for Accurate Rough Terrain Estimation

Martial Hebert, Raia Hadsell, Daniel F. Huber, 2010, Int. J. Robotics Res..

Provably Efficient Imitation Learning from Observation Alone pdf

Byron Boots, J. Andrew Bagnell, Anirudh Vemula, 2019, ICML.

Predicting Contextual Sequences via Submodular Function Maximization pdf

Martial Hebert, J. Andrew Bagnell, Debadeepta Dey, 2012, ArXiv.

Generalized Boosting Algorithms for Convex Optimization pdf

J. Andrew Bagnell, Alexander Grubb, J. Bagnell, 2011, ICML.

Kernel Conjugate Gradient for Fast Kernel Machines

J. Andrew Bagnell, Nathan D. Ratliff, J. Bagnell, 2007, IJCAI.

Shared autonomy via hindsight optimization for teleoperation and teaming

Siddhartha S. Srinivasa, J. Andrew Bagnell, Stefania Pellegrinelli, 2017, Int. J. Robotics Res..

Efficient 3-D scene analysis from streaming data

Martial Hebert, J. Andrew Bagnell, Daniel Munoz, 2013, 2013 IEEE International Conference on Robotics and Automation.

Planning-based prediction for pedestrians

Siddhartha S. Srinivasa, Martial Hebert, Anind K. Dey, 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Anytime online novelty detection for vehicle safeguarding

J. Andrew Bagnell, Anthony Stentz, Boris Sofman, 2010, 2010 IEEE International Conference on Robotics and Automation.

Learning with Scope, with Application to Information Extraction and Classification

Andrew McCallum, David M. Blei, J. Andrew Bagnell, 2002, UAI.

GATMO: A Generalized Approach to Tracking Movable Objects

Siddhartha S. Srinivasa, J. Andrew Bagnell, Dave Ferguson, 2009, 2009 IEEE International Conference on Robotics and Automation.

Human Behavior Modeling with Maximum Entropy Inverse Optimal Control

Anind K. Dey, J. Andrew Bagnell, Brian D. Ziebart, 2009, AAAI Spring Symposium: Human Behavior Modeling.

Stability Conditions for Online Learnability pdf

J. Andrew Bagnell, Stéphane Ross, 2011, ArXiv.

Convex Coding pdf

David M. Bradley, J. Andrew Bagnell, J. Bagnell, 2009, UAI.

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

J. Andrew Bagnell, Anthony Stentz, Moslem Kazemi, 2013, Auton. Robots.

Online Instrumental Variable Regression with Applications to Online Linear System Identification

Byron Boots, Martial Hebert, J. Andrew Bagnell, 2016, AAAI.

Learning for Autonomous Navigation

David M. Bradley, David Silver, J. Andrew Bagnell, 2010, IEEE Robotics & Automation Magazine.

Gradient Boosting on Stochastic Data Streams

Martial Hebert, J. Andrew Bagnell, Arun Venkatraman, 2017, AISTATS.

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

J. Andrew Bagnell, Maxim Likhachev, Anirudh Vemula, 2020, ArXiv.

Robust Monocular Flight in Cluttered Outdoor Environments pdf

Martial Hebert, J. Andrew Bagnell, Debadeepta Dey, 2016, ArXiv.

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

J. Andrew Bagnell, Anthony Stentz, Moslem Kazemi, 2013, Auton. Robots.

Improving Robot Navigation Through Self-Supervised Online Learning

J. Andrew Bagnell, Anthony Stentz, Ellie Lin, 2006, Robotics: Science and Systems.

Probabilistic pointing target prediction via inverse optimal control

Anind K. Dey, J. Andrew Bagnell, Brian D. Ziebart, 2012, IUI '12.

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

Anind K. Dey, J. Andrew Bagnell, Brian D. Ziebart, 2008, UbiComp.

High Performance Outdoor Navigation from Overhead Data using Imitation Learning

David Silver, J. Andrew Bagnell, Anthony Stentz, 2008, Robotics: Science and Systems.

CHOMP: Covariant Hamiltonian optimization for motion planning

Siddhartha S. Srinivasa, Anca D. Dragan, J. Andrew Bagnell, 2013, Int. J. Robotics Res..

Reinforcement Planning: RL for optimal planners

J. Andrew Bagnell, Matthew Zucker, 2012, 2012 IEEE International Conference on Robotics and Automation.

Predictive-State Decoders: Encoding the Future into Recurrent Networks

Byron Boots, Martial Hebert, J. Andrew Bagnell, 2017, NIPS.

Robot Learning

Stefan Schaal, Daniel D. Lee, Jan Peters, 2017, Encyclopedia of Machine Learning and Data Mining.

Submodular Surrogates for Value of Information

Andreas Krause, Siddhartha S. Srinivasa, Amin Karbasi, 2015, AAAI.

Semi-Autonomous Manipulation of Natural Objects

J. Andrew Bagnell, Anthony Stentz, Moslem Kazemi, 2012 .

Efficient Feature Group Sequencing for Anytime Linear Prediction pdf

Martial Hebert, J. Andrew Bagnell, Alexander Grubb, 2014, UAI.

Solving Uncertain Markov Decision Processes

Andrew Y. Ng, J. Andrew Bagnell, Jeff Schneider, 2001 .

Imitation learning for locomotion and manipulation

Siddhartha S. Srinivasa, J. Andrew Bagnell, Nathan D. Ratliff, 2007, 2007 7th IEEE-RAS International Conference on Humanoid Robots.

Efficient Optimization for Autonomous Robotic Manipulation of Natural Objects

Abdeslam Boularias, J. Andrew Bagnell, Anthony Stentz, 2014, AAAI.

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

Martial Hebert, J. Andrew Bagnell, Xuehan Xiong, 2011, 2011 IEEE International Conference on Robotics and Automation.

Maximum margin planning

J. Andrew Bagnell, Martin Zinkevich, Nathan D. Ratliff, 2006, ICML.

Near Optimal Bayesian Active Learning for Decision Making pdf

Andreas Krause, Siddhartha S. Srinivasa, Amin Karbasi, 2014, AISTATS.

Autonomy infused teleoperation with application to brain computer interface controlled manipulation

Martial Hebert, Jeffrey Weiss, Katharina Mülling, 2017, Autonomous Robots.

Contextual Sequence Prediction via Submodular Function Optimization

Martial Hebert, J. Andrew Bagnell, Debadeepta Dey, 2012 .

Anytime Neural Network: a Versatile Trade-off Between Computation and Accuracy

Martial Hebert, J. Andrew Bagnell, Debadeepta Dey, 2018 .

An integrated system for autonomous robotics manipulation

Martial Hebert, J. Andrew Bagnell, Lei Cui, 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Autonomous helicopter control using reinforcement learning policy search methods

Jeff G. Schneider, J. Andrew Bagnell, J. Bagnell, 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

Agnostic System Identification for Model-Based Reinforcement Learning pdf

J. Andrew Bagnell, Stéphane Ross, J. Bagnell, 2012, ICML.

Policy Gradient Methods

Jan Peters, J. Andrew Bagnell, Jan Peters, 2010, Encyclopedia of Machine Learning.

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

J. Andrew Bagnell, Anirudh Vemula, Wen Sun, 2019, AISTATS.

CHOMP: Gradient optimization techniques for efficient motion planning

Siddhartha S. Srinivasa, J. Andrew Bagnell, Nathan D. Ratliff, 2009, 2009 IEEE International Conference on Robotics and Automation.

Applied Imitation Learning for Autonomous Navigation in Complex Natural Terrain

David Silver, J. Andrew Bagnell, Anthony Stentz, 2009, FSR.

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

Sebastian Scherer, J. Andrew Bagnell, Sanjiban Choudhury, 2015, Robotics: Science and Systems.

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

J. Andrew Bagnell, Matthew T. Mason, Jiaji Zhou, 2017, Robotics: Science and Systems.

Closed-loop Servoing using Real-time Markerless Arm Tracking

J. Andrew Bagnell, Moslem Kazemi, Nancy S. Pollard, 2013 .

Learning Policies for Contextual Submodular Prediction

Yisong Yue, J. Andrew Bagnell, Debadeepta Dey, 2013, ICML.

Learning Transferable Policies for Monocular Reactive MAV Control

Martial Hebert, J. Andrew Bagnell, Shreyansh Daftry, 2016, ISER.

Adaptive workspace biasing for sampling-based planners

James J. Kuffner, J. Andrew Bagnell, Matthew Zucker, 2008, 2008 IEEE International Conference on Robotics and Automation.

CHIMP, the CMU Highly Intelligent Mobile Platform

Siddhartha S. Srinivasa, Kyle Strabala, Michael Vande Weghe, 2015, J. Field Robotics.

Strategy Transfer Learning via Parametric Deviation Sets

Kevin Waugh, Geoffrey J. Gordon, Katia Sycara, 2011 .

Computational Rationalization: The Inverse Equilibrium Problem pdf

Kevin Waugh, J. Andrew Bagnell, Brian D. Ziebart, 2011, ICML.

Predicting Multiple Structured Visual Interpretations

Varun Ramakrishna, Martial Hebert, J. Andrew Bagnell, 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

Policy gradient methods

Jan Peters, J. Andrew Bagnell, Jan Peters, 2010, Scholarpedia.

On Local Rewards and Scaling Distributed Reinforcement Learning

Andrew Y. Ng, J. Andrew Bagnell, 2005, NIPS.

Differentiable Sparse Coding

David M. Bradley, J. Andrew Bagnell, J. Bagnell, 2008, NIPS.

Differential Sparse Coding

J. Andrew Bagnell, David Mcallister Bradley, J. Bagnell, 2008 .

Efficient touch based localization through submodularity

Siddhartha S. Srinivasa, J. Andrew Bagnell, Nancy S. Pollard, 2012, 2013 IEEE International Conference on Robotics and Automation.

Introspective perception: Learning to predict failures in vision systems

Martial Hebert, J. Andrew Bagnell, Shreyansh Daftry, 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

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

J. Andrew Bagnell, Matthew T. Mason, Jiaji Zhou, 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

No-Regret Reductions for Imitation Learning and Structured Prediction pdf

Geoffrey J. Gordon, J. Andrew Bagnell, Stéphane Ross, 2010, ArXiv.

Covariant policy search

Jeff G. Schneider, J. Andrew Bagnell, J. Bagnell, 2003, IJCAI 2003.

Learning message-passing inference machines for structured prediction

Martial Hebert, J. Andrew Bagnell, Stéphane Ross, 2011, CVPR 2011.

Online Bellman Residual Algorithms with Predictive Error Guarantees

J. Andrew Bagnell, Wen Sun, 2015, UAI.

Improving robot navigation through self‐supervised online learning

J. Andrew Bagnell, Anthony Stentz, Ellie Lin, 2006, J. Field Robotics.

Log-DenseNet: How to Sparsify a DenseNet pdf

Martial Hebert, J. Andrew Bagnell, Debadeepta Dey, 2017, ArXiv.

Minimizing user cost for shared autonomy

Siddhartha S. Srinivasa, J. Andrew Bagnell, Shervin Javdani, 2016, 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

Online Bellman Residual and Temporal Difference Algorithms with Predictive Error Guarantees

J. Andrew Bagnell, Wen Sun, J. Bagnell, 2016, IJCAI.

(Approximate) Subgradient Methods for Structured Prediction

J. Andrew Bagnell, Martin Zinkevich, Nathan D. Ratliff, 2007, International Conference on Artificial Intelligence and Statistics.

Active learning from demonstration for robust autonomous navigation

David Silver, J. Andrew Bagnell, Anthony Stentz, 2012, 2012 IEEE International Conference on Robotics and Automation.

An optimization approach to rough terrain locomotion

Christopher G. Atkeson, James J. Kuffner, J. Andrew Bagnell, 2010, 2010 IEEE International Conference on Robotics and Automation.

Perceptual Interpretation for Autonomous Navigation through Dynamic Imitation Learning

David Silver, J. Andrew Bagnell, Anthony Stentz, 2009, ISRR.

Co-inference for Multi-modal Scene Analysis

Martial Hebert, J. Andrew Bagnell, Daniel Munoz, 2012, ECCV.

Pose Machines: Articulated Pose Estimation via Inference Machines

Varun Ramakrishna, Yaser Sheikh, Martial Hebert, 2014, ECCV.

Reinforcement learning in robotics: A survey

Jan Peters, J. Andrew Bagnell, Jens Kober, 2013, Int. J. Robotics Res..

Boosting Structured Prediction for Imitation Learning

David M. Bradley, Joel E. Chestnutt, J. Andrew Bagnell, 2006, NIPS.

Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification

Anind K. Dey, J. Andrew Bagnell, Brian D. Ziebart, 2007, UAI.

Optimization and learning for rough terrain legged locomotion

Christopher G. Atkeson, Joel E. Chestnutt, James J. Kuffner, 2011, Int. J. Robotics Res..

A Discriminative Framework for Anomaly Detection in Large Videos pdf

Martial Hebert, J. Andrew Bagnell, Allison Del Giorno, 2016, ECCV.

Editorial: Special Issue on Machine Learning in Robotics

Stefan Schaal, J. Andrew Bagnell, S. Schaal, 2008, Int. J. Robotics Res..

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

Martial Hebert, J. Andrew Bagnell, Nicholas Rhinehart, 2014, 2015 IEEE International Conference on Robotics and Automation (ICRA).

Dual Policy Iteration

Byron Boots, Geoffrey J. Gordon, J. Andrew Bagnell, 2018, NeurIPS.

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

J. Andrew Bagnell, Anthony Stentz, Moslem Kazemi, 2013, 2013 IEEE International Conference on Robotics and Automation.

Bandit-Based Online Candidate Selection for Adjustable Autonomy

J. Andrew Bagnell, Anthony Stentz, Boris Sofman, 2009, FSR.

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

Geoffrey J. Gordon, J. Andrew Bagnell, Stéphane Ross, 2010, AISTATS.

Contextual classification with functional Max-Margin Markov Networks

Martial Hebert, J. Andrew Bagnell, Nicolas Vandapel, 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

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

Martial Hebert, J. Andrew Bagnell, Allison Del Giorno, 2017, ArXiv.

Fast Planning for Dynamic Preferences

Anind K. Dey, J. Andrew Bagnell, Brian D. Ziebart, 2008, ICAPS.

An Algorithmic Perspective on Imitation Learning

Pieter Abbeel, Jan Peters, J. Andrew Bagnell, 2018, Found. Trends Robotics.

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

J. Andrew Bagnell, Kris M. Kitani, De-An Huang, 2015, AAAI.

Learning to search: Functional gradient techniques for imitation learning

David Silver, J. Andrew Bagnell, Nathan D. Ratliff, 2009, Auton. Robots.

Tartan Racing: A multi-modal approach to the DARPA Urban Challenge

William Whittaker, Martial Hebert, Alonzo Kelly, 2007 .

Digital Soil Mapping of Soil Properties in Honduras Using Readily Available Biophysical Datasets and Gaussian Processes

J. Andrew Bagnell, Simon E. Cook, Juan Pablo Gonzalez, 2008 .

Efficient high dimensional maximum entropy modeling via symmetric partition functions

J. Andrew Bagnell, Paul Vernaza, J. Bagnell, 2012, NIPS.

Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain

David Silver, J. Andrew Bagnell, Anthony Stentz, 2010, Int. J. Robotics Res..

Anytime Neural Networks via Joint Optimization of Auxiliary Losses pdf

Martial Hebert, J. Andrew Bagnell, Debadeepta Dey, 2017, ArXiv.

Learning Autonomous Driving Styles and Maneuvers from Expert Demonstration

David Silver, J. Andrew Bagnell, Anthony Stentz, 2012, ISER.

A Probabilistic Planning Framework for Planar Grasping Under Uncertainty

Aaron M. Johnson, J. Andrew Bagnell, Matthew T. Mason, 2017, IEEE Robotics and Automation Letters.

Autonomous driving in urban environments: Boss and the Urban Challenge

William Whittaker, Young-Woo Seo, Martial Hebert, 2008, J. Field Robotics.

Human-inspired force compliant grasping primitives

J. Andrew Bagnell, Moslem Kazemi, Nancy S. Pollard, 2014, Auton. Robots.

Clearing a pile of unknown objects using interactive perception

J. Andrew Bagnell, Anthony Stentz, Moslem Kazemi, 2013, 2013 IEEE International Conference on Robotics and Automation.

Reinforcement and Imitation Learning via Interactive No-Regret Learning pdf

J. Andrew Bagnell, Stéphane Ross, J. Bagnell, 2014, ArXiv.

Learning Opportunity Costs in Multi-Robot Market Based Planners

Reid G. Simmons, Jeff G. Schneider, J. Andrew Bagnell, 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

Learning to search: structured prediction techniques for imitation learning

Nathan Ratliff, J. Andrew Bagnell, Nathan D. Ratliff, 2009 .

Autonomous driving in urban environments: Boss and the Urban Challenge

William Whittaker, Young-Woo Seo, Martial Hebert, 2008, J. Field Robotics.

Truncated Horizon Policy Search: Combining Reinforcement Learning & Imitation Learning pdf

Byron Boots, J. Andrew Bagnell, Wen Sun, 2018, ICLR.

Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy

J. Andrew Bagnell, Brian D. Ziebart, J. Bagnell, 2010 .

Accurate rough terrain estimation with space-carving kernels

Martial Hebert, Raia Hadsell, Daniel F. Huber, 2009, Robotics: Science and Systems.

Stacked Hierarchical Labeling

Martial Hebert, J. Andrew Bagnell, Daniel Munoz, 2010, ECCV.

Efficient Reductions for Imitation Learning

J. Andrew Bagnell, Stéphane Ross, J. Bagnell, 2010, AISTATS.

Improving Multi-Step Prediction of Learned Time Series Models

Martial Hebert, J. Andrew Bagnell, Arun Venkatraman, 2015, AAAI.

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

J. Andrew Bagnell, Sanjiban Choudhury, Brian D. Ziebart, 2021, ArXiv.

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

J. Andrew Bagnell, Sanjiban Choudhury, Gokul Swamy, 2021, ICML.

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

J. Andrew Bagnell, Maxim Likhachev, Anirudh Vemula, 2021, AAAI.

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

Zhiwei Steven Wu, J. Andrew Bagnell, Sanjiban Choudhury, 2021, ArXiv.

A Critique of Strictly Batch Imitation Learning pdf

Zhiwei Steven Wu, J. Andrew Bagnell, Sanjiban Choudhury, 2021, ArXiv.

Creating Low-Cost Soil Maps for Tropical Agriculture Using Gaussian Processes

J. Andrew Bagnell, Simon E. Cook, Juan Pablo Gonzalez, 2007 .

Planning and Execution using Inaccurate Models with Provable Guarantees pdf

J. Andrew Bagnell, Maxim Likhachev, Anirudh Vemula, 2020, Robotics: Science and Systems.

Domain Adaptation For Mobile Robot Navigation

J. Andrew Bagnell, David M. Bradley, 2009 .