J. Bagnell

发表

Maximum Entropy Inverse Reinforcement Learning

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

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).

Improving Multi-Step Prediction of Learned Time Series Models

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

Covariant Policy Search

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

Autonomous Helicopter Control using Reinforcement Learning

J. Bagnell, J. Schneider, 2001 .

Policy Gradient Methods

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

Learning decisions: robustness, uncertainty, and approximation

A. Moore, J. Bagnell, J. Schneider, 2004 .

Learning for Autonomous Navigation

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

Policy search in kernel Hilbert space

J. Bagnell, J. Schneider, 2003 .

Reinforcement learning in robotics: A survey

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

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

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

Subgradient Methods for Maximum Margin Structured Learning

Nathan D. Ratliff, Martin A. Zinkevich, J. Bagnell, 2006 .

Maximum margin planning

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

Policy Search by Dynamic Programming

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

Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy

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

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.

Autonomous driving in urban environments: Boss and the Urban Challenge

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

CHOMP: Covariant Hamiltonian optimization for motion planning

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

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.

Boosting Structured Prediction for Imitation Learning

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

High Performance Outdoor Navigation from Overhead Data using Imitation Learning

David Silver, J. Andrew Bagnell, Anthony Stentz, 2008, 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.

Modeling Interaction via the Principle of Maximum Causal Entropy

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

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

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

Efficient Reductions for Imitation Learning

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

Autonomy Infused Teleoperation with Application to BCI Manipulation pdf

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

Online) Subgradient Methods for Structured Prediction

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

Learning to search: Functional gradient techniques for imitation learning

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

Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction

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

Agnostic System Identification for Model-Based Reinforcement Learning pdf

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

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).

Improved Learning of Dynamics Models for Control

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

DATA AS DEMONSTRATOR with Applications to System Identification

M. Hebert, J. Bagnell, Arun, 2014 .

Adaptive workspace biasing for sampling-based planners

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

Learning in modular systems

James Andrew Bagnell, David Mcallister Bradley, J. Bagnell, 2010 .

No-Regret Reductions for Imitation Learning and Structured Prediction pdf

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

Differentiable Sparse Coding

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

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).

Process-Based Modeling

Stan Matwin, Eyke Hüllermeier, Johannes Fürnkranz, 2010, Encyclopedia of Machine Learning.

An Algorithmic Perspective on Imitation Learning

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

Shared Autonomy via Hindsight Optimization

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

Learning with Scope, with Application to Information Extraction and Classification

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

Activity Forecasting

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

Predicting Multiple Structured Visual Interpretations

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

Efficient Reductions for Imitation Learning Supplementary Material

J. Bagnell, Stéphane Ross, 2010 .

Stacked Hierarchical Labeling

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

Planning-based prediction for pedestrians

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

Improving Robot Navigation Through Self-Supervised Online Learning

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

Imitation Learning for Natural Language Direction Following

N. Roy, J. Bagnell, Alexander Grubb, 2011 .

Efficient temporal consistency for streaming video scene analysis

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

(Approximate) Subgradient Methods for Structured Prediction

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

Inverse Optimal Heuristic Control for Imitation Learning

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

Kernel Conjugate Gradient

Nathan D. Ratliff, J. Bagnell, 2005 .

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

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

Vision and Learning for Deliberative Monocular Cluttered Flight pdf

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

Maximum Causal Entropy Correlated Equilibria for Markov Games

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

Learning to Filter with Predictive State Inference Machines pdf

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

Learning Deep Inference Machines

J. Bagnell, Stéphane Ross, Daniel Munoz, 2010 .

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.

Probabilistic pointing target prediction via inverse optimal control

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

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.

Efficient 3-D scene analysis from streaming data

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

Anytime online novelty and change detection for mobile robots

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

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.

Generalized Boosting Algorithms for Convex Optimization pdf

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

Learning message-passing inference machines for structured prediction

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

Detecting Interesting Events Using Unsupervised Density Ratio Estimation

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

Solving Uncertain Markov Decision Processes

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

Efficient Feature Group Sequencing for Anytime Linear Prediction pdf

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

Inference Machines for Nonparametric Filter Learning

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

Submodular Surrogates for Value of Information

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

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).

Stabilizing Human Control Strategies through Reinforcement Learning

J. Bagnell, M. Nechyba, 1999 .

Learning to search: structured prediction techniques for imitation learning

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

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.

Shared autonomy via hindsight optimization for teleoperation and teaming

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

Autonomy infused teleoperation with application to brain computer interface controlled manipulation

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

Space-carving Kernels for Accurate Rough Terrain Estimation

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

Contextual classification with functional Max-Margin Markov Networks

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

Accurate rough terrain estimation with space-carving kernels

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

Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain

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

Improving robot navigation through self‐supervised online learning

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

Robust Supervised Learning

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

Robust Monocular Flight in Cluttered Outdoor Environments pdf

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

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

William Whittaker, Martial Hebert, Alonzo Kelly, 2007 .

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.

Strategy Transfer Learning via Parametric Deviation Sets

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

Efficient Optimization for Autonomous Robotic Manipulation of Natural Objects

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

Autonomous driving in urban environments: Boss and the Urban Challenge

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

Reinforcement Planning : Planners as Policies

J. Bagnell, Matthew Zucker, 2010 .

Learning User Models During Shared Autonomy

S. Srinivasa, J. Bagnell, Shervin Javdani, 2016 .

Approximate MaxEnt Inverse Optimal Control

Amir-massoud Farahmand, J. Bagnell, De-An Huang, 2015 .

A Robot that Teaches Itself to Clear Clutter

J. Bagnell, A. Stentz, Abdeslam Boularias, 2016 .

Human-Inspired Force Compliant Grasping Primitives Moslem Kazemi ·

J. Bagnell, N. Pollard, Jean-Sebastien Valois, 2014 .

Training Procedures for Overlabelled Data

J. Bagnell, K. Waugh, 2013 .

Learning Positive Functions in a Hilbert Space

Amir-massoud Farahmand, J. Bagnell, 2015 .

Decision Region Determination for Touch Based Localization

S. Srinivasa, Andreas Krause, J. Bagnell, 2014 .

Efficient touch based localization through submodularity

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

Efficient Task Dependent Localization through Submodularity

S. Srinivasa, J. Bagnell, Shervin Javdani, 2013 .

Fast Planning for Dynamic Preferences

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

Contextual Sequence Prediction via Submodular Function Optimization

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

Policy gradient methods

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

Editorial: Special Issue on Machine Learning in Robotics

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

Novelty Detection for Vehicle Safeguarding

J. Bagnell, Boris Sofman, A. Stentz, 2010 .

Online Novelty Detection for Vehicle Safeguarding

J. Bagnell, Boris Sofman, A. Stentz, 2009 .

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

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

Contextual classification with functional Max-Margin Markov Networks

M. Hebert, J. Bagnell, N. Vandapel, 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

Robot Learning

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

Predictive software models

Stan Matwin, Eyke Hüllermeier, Johannes Fürnkranz, 2004, Conference on Semantics in Text Processing.

Reinforcement learning in robotics: A survey

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

Sequence Model Imitation Learning with Unobserved Contexts

Zhiwei Steven Wu, J. Bagnell, Sanjiban Choudhury, 2022, 2208.02225.

Game-Theoretic Algorithms for Conditional Moment Matching

Zhiwei Steven Wu, J. Bagnell, Sanjiban Choudhury, 2022, ArXiv.

Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

J. Bagnell, A. Krishnamurthy, Ayush Sekhari, 2022, ArXiv.

Sequence Model Imitation Learning with Unobserved Contexts

Zhiwei Steven Wu, J. Bagnell, Sanjiban Choudhury, 2022, NeurIPS.

Human-inspired force compliant grasping primitives

J. Bagnell, N. Pollard, Jean-Sebastien Valois, 2014, Autonomous Robots.

Autonomy infused teleoperation with application to brain computer interface controlled manipulation

A. Schwartz, M. Hebert, J. Bagnell, 2017, Autonomous Robots.

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

J. Bagnell, Arun Venkatraman, A. Stentz, 2014, Autonomous Robots.

Convex Coding pdf

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

Differential Sparse Coding

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

Planning and Execution using Inaccurate Models with Provable Guarantees pdf

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

Segmentation-based online change detection for mobile robots

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

Gradient Boosting on Stochastic Data Streams

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

Learning to Manipulate Unknown Objects in Clutter by Reinforcement

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

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.

Anytime online novelty detection for vehicle safeguarding

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

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.

An Invitation to Imitation

J A Bagnell, J. Bagnell, 2015 .

Provably Efficient Imitation Learning from Observation Alone pdf

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

A Discriminative Framework for Anomaly Detection in Large Videos pdf

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

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).

Pose Machines: Articulated Pose Estimation via Inference Machines

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

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.

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..

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

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

Computational Rationalization: The Inverse Equilibrium Problem pdf

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

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.

Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

J. Bagnell, A. Krishnamurthy, Ayush Sekhari, 2022, ICLR.

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.

Robust Object Grasping using Force Compliant Motion Primitives

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

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

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

Feedback in Imitation Learning: The Three Regimes of Covariate Shift

Brian D. Ziebart, J. Bagnell, Arun Venkatraman, 2021, ArXiv.

Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing

Debadeepta Dey, Hanzhang Hu, J. Andrew Bagnell, 2017, AAAI.

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

J. Bagnell, Aarti Singh, Sanjiban Choudhury, 2023, ICML.

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.

Efficient high dimensional maximum entropy modeling via symmetric partition functions

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

Active learning from demonstration for robust autonomous navigation

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

Dual Policy Iteration

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

Human-inspired force compliant grasping primitives

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

Optimization and learning for rough terrain legged locomotion

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

Co-inference for Multi-modal Scene Analysis

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

Learning Transferable Policies for Monocular Reactive MAV Control

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

Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification

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

Blending of brain-machine interface and vision-guided autonomous robotics improves neuroprosthetic arm performance during grasping

Martial Hebert, Katharina Muelling, Arun Venkatraman, 2016, Journal of NeuroEngineering and Rehabilitation.

Reinforcement and Imitation Learning via Interactive No-Regret Learning pdf

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

Learning Policies for Contextual Submodular Prediction

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

Log-DenseNet: How to Sparsify a DenseNet pdf

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

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

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

Closed-loop Servoing using Real-time Markerless Arm Tracking

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

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

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

CHIMP, the CMU Highly Intelligent Mobile Platform

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

Solving Games with Functional Regret Estimation

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

Activity Forecasting

Kris M. Kitani, Brian D. Ziebart, M. Hebert, 2012, ECCV.

Applied Imitation Learning for Autonomous Navigation in Complex Natural Terrain

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

Semantic perception for ground robotics

K. Daniilidis, L. Mianzo, M. Hebert, 2012, Defense, Security, and Sensing.

Near Optimal Bayesian Active Learning for Decision Making pdf

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

Causal Imitation Learning under Temporally Correlated Noise

Zhiwei Steven Wu, J. Bagnell, Sanjiban Choudhury, 2022, ICML.

Contextual Sequence Prediction with Application to Control Library Optimization

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

Online Instrumental Variable Regression with Applications to Online Linear System Identification

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

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.

Maximum Entropy Inverse Reinforcement Learning

Brian D. Ziebart, Andrew L. Maas, J. Bagnell, 2008, AAAI.

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.

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.

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.

Online Bellman Residual and Temporal Difference Algorithms with Predictive Error Guarantees

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

Predictive-State Decoders: Encoding the Future into Recurrent Networks

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

Inverse Reinforcement Learning without Reinforcement Learning

Zhiwei Steven Wu, J. Bagnell, Sanjiban Choudhury, 2023, ICML.

Efficient Optimization of Control Libraries

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

Kernel Conjugate Gradient for Fast Kernel Machines

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

Learning to Smooth with Bidirectional Predictive State Inference Machines

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

Maximum Entropy Inverse Reinforcement Learning

Brian D. Ziebart, J. Bagnell, A. Dey, 2008, AAAI.

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

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

Movement primitives via optimization

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

REBEL: Reinforcement Learning via Regressing Relative Rewards

J. Bagnell, Kianté Brantley, Gokul Swamy, 2024, 2404.16767.

Encyclopedia of Machine Learning and Data Mining

Peter A. Flach, Petra Kralj Novak, Cecilia M. Procopiuc, 2017, Encyclopedia of Machine Learning and Data Mining.

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

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

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

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