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ORCIDJ. Andrew Bagnell
James A. Bagnell – James Andrew Bagnell – Drew Bagnell
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- affiliation: Carnegie Mellon University, Pittsburgh, USA
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2020 – today
- 2024
[c122]Yuda Song, Drew Bagnell, Aarti Singh:
Hybrid Reinforcement Learning from Offline Observation Alone. ICML 2024- [c121]Juntao Ren, Gokul Swamy, Steven Wu, Drew Bagnell, Sanjiban Choudhury:
Hybrid Inverse Reinforcement Learning. ICML 2024 - [i62]Juntao Ren, Gokul Swamy, Zhiwei Steven Wu, J. Andrew Bagnell, Sanjiban Choudhury:
Hybrid Inverse Reinforcement Learning. CoRR abs/2402.08848 (2024) - [i61]Zhaolin Gao, Jonathan D. Chang, Wenhao Zhan, Owen Oertell, Gokul Swamy, Kianté Brantley, Thorsten Joachims, J. Andrew Bagnell, Jason D. Lee, Wen Sun:
REBEL: Reinforcement Learning via Regressing Relative Rewards. CoRR abs/2404.16767 (2024) - [i60]Yuda Song, Gokul Swamy, Aarti Singh, J. Andrew Bagnell, Wen Sun:
Understanding Preference Fine-Tuning Through the Lens of Coverage. CoRR abs/2406.01462 (2024) - [i59]Yuda Song, J. Andrew Bagnell, Aarti Singh:
Hybrid Reinforcement Learning from Offline Observation Alone. CoRR abs/2406.07253 (2024) - 2023
- [c120]Yuda Song, Yifei Zhou, Ayush Sekhari, Drew Bagnell, Akshay Krishnamurthy, Wen Sun:
Hybrid RL: Using both offline and online data can make RL efficient. ICLR 2023 - [c119]Gokul Swamy, David Wu, Sanjiban Choudhury, Drew Bagnell, Zhiwei Steven Wu:
Inverse Reinforcement Learning without Reinforcement Learning. ICML 2023: 33299-33318 - [c118]Anirudh Vemula, Yuda Song, Aarti Singh, Drew Bagnell, Sanjiban Choudhury:
The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms. ICML 2023: 34978-35005 - [i58]Anirudh Vemula, Yuda Song, Aarti Singh, J. Andrew Bagnell, Sanjiban Choudhury:
The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms. CoRR abs/2303.00694 (2023) - [i57]Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu:
Inverse Reinforcement Learning without Reinforcement Learning. CoRR abs/2303.14623 (2023) - 2022
- [c117]Gokul Swamy, Sanjiban Choudhury, Drew Bagnell, Steven Wu:
Causal Imitation Learning under Temporally Correlated Noise. ICML 2022: 20877-20890 - [c116]Anirudh Vemula, Wen Sun, Maxim Likhachev, J. Andrew Bagnell:
On the Effectiveness of Iterative Learning Control. L4DC 2022: 47-58 - [c115]Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu:
Sequence Model Imitation Learning with Unobserved Contexts. NeurIPS 2022 - [c114]Gokul Swamy, Nived Rajaraman, Matthew Peng, Sanjiban Choudhury, J. Andrew Bagnell, Steven Wu, Jiantao Jiao, Kannan Ramchandran:
Minimax Optimal Online Imitation Learning via Replay Estimation. NeurIPS 2022 - [i56]Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu:
Causal Imitation Learning under Temporally Correlated Noise. CoRR abs/2202.01312 (2022) - [i55]Gokul Swamy, Nived Rajaraman, Matthew Peng, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu, Jiantao Jiao, Kannan Ramchandran:
Minimax Optimal Online Imitation Learning via Replay Estimation. CoRR abs/2205.15397 (2022) - [i54]Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu:
Sequence Model Imitation Learning with Unobserved Contexts. CoRR abs/2208.02225 (2022) - [i53]Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu:
Game-Theoretic Algorithms for Conditional Moment Matching. CoRR abs/2208.09551 (2022) - [i52]Yuda Song, Yifei Zhou, Ayush Sekhari, J. Andrew Bagnell, Akshay Krishnamurthy, Wen Sun:
Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient. CoRR abs/2210.06718 (2022) - 2021
- [c113]Anirudh Vemula, J. Andrew Bagnell, Maxim Likhachev:
CMAX++ : Leveraging Experience in Planning and Execution using Inaccurate Models. AAAI 2021: 6147-6155 - [c112]Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Steven Wu:
Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap. ICML 2021: 10022-10032 - [i51]Jonathan C. Spencer, Sanjiban Choudhury, Arun Venkatraman, Brian D. Ziebart, J. Andrew Bagnell:
Feedback in Imitation Learning: The Three Regimes of Covariate Shift. CoRR abs/2102.02872 (2021) - [i50]Gokul Swamy, Sanjiban Choudhury, Zhiwei Steven Wu, J. Andrew Bagnell:
Of Moments and Matching: Trade-offs and Treatments in Imitation Learning. CoRR abs/2103.03236 (2021) - [i49]Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu:
A Critique of Strictly Batch Imitation Learning. CoRR abs/2110.02063 (2021) - [i48]Anirudh Vemula, Wen Sun, Maxim Likhachev, J. Andrew Bagnell:
On the Effectiveness of Iterative Learning Control. CoRR abs/2111.09434 (2021) - 2020
- [c111]Anirudh Vemula, J. Andrew Bagnell:
Tron: A Fast Solver for Trajectory Optimization with Non-Smooth Cost Functions. CDC 2020: 4157-4163 - [c110]Anirudh Vemula, Yash Oza, J. Andrew Bagnell, Maxim Likhachev:
Planning and Execution using Inaccurate Models with Provable Guarantees. Robotics: Science and Systems 2020 - [i47]Anirudh Vemula, Yash Oza, J. Andrew Bagnell, Maxim Likhachev:
Planning and Execution using Inaccurate Models with Provable Guarantees. CoRR abs/2003.04394 (2020) - [i46]Anirudh Vemula, J. Andrew Bagnell:
TRON: A Fast Solver for Trajectory Optimization with Non-Smooth Cost Functions. CoRR abs/2003.14393 (2020) - [i45]Anirudh Vemula, Wen Sun, J. Andrew Bagnell:
Exploration in Action Space. CoRR abs/2004.00500 (2020) - [i44]Anirudh Vemula, J. Andrew Bagnell, Maxim Likhachev:
CMAX++ : Leveraging Experience in Planning and Execution using Inaccurate Models. CoRR abs/2009.09942 (2020)
2010 – 2019
- 2019
- [c109]Hanzhang Hu, Debadeepta Dey, Martial Hebert, J. Andrew Bagnell:
Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing. AAAI 2019: 3812-3821 - [c108]Anirudh Vemula, Wen Sun, J. Andrew Bagnell:
Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective. AISTATS 2019: 2926-2935 - [c107]Wen Sun, Anirudh Vemula, Byron Boots, Drew Bagnell:
Provably Efficient Imitation Learning from Observation Alone. ICML 2019: 6036-6045 - [i43]Anirudh Vemula, Wen Sun, J. Andrew Bagnell:
Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective. CoRR abs/1901.11503 (2019) - [i42]Wen Sun, Anirudh Vemula, Byron Boots, J. Andrew Bagnell:
Provably Efficient Imitation Learning from Observation Alone. CoRR abs/1905.10948 (2019) - 2018
- [j20]Takayuki Osa
, Joni Pajarinen, Gerhard Neumann, J. Andrew Bagnell, Pieter Abbeel, Jan Peters:
An Algorithmic Perspective on Imitation Learning. Found. Trends Robotics 7(1-2): 1-179 (2018) - [j19]Jiaji Zhou, Matthew T. Mason, Robert Paolini, Drew Bagnell:
A convex polynomial model for planar sliding mechanics: theory, application, and experimental validation. Int. J. Robotics Res. 37(2-3): 249-265 (2018) - [j18]Shervin Javdani, Henny Admoni, Stefania Pellegrinelli, Siddhartha S. Srinivasa, J. Andrew Bagnell:
Shared autonomy via hindsight optimization for teleoperation and teaming. Int. J. Robotics Res. 37(7): 717-742 (2018) - [c106]Wen Sun, J. Andrew Bagnell, Byron Boots:
Truncated horizon Policy Search: Combining Reinforcement Learning & Imitation Learning. ICLR (Poster) 2018 - [c105]Wen Sun, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell:
Dual Policy Iteration. NeurIPS 2018: 7059-7069 - [i41]Wen Sun, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell:
Dual Policy Iteration. CoRR abs/1805.10755 (2018) - [i40]Wen Sun, J. Andrew Bagnell, Byron Boots:
Truncated Horizon Policy Search: Combining Reinforcement Learning & Imitation Learning. CoRR abs/1805.11240 (2018) - [i39]Takayuki Osa, Joni Pajarinen, Gerhard Neumann, J. Andrew Bagnell, Pieter Abbeel, Jan Peters:
An Algorithmic Perspective on Imitation Learning. CoRR abs/1811.06711 (2018) - 2017
- [j17]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:
Autonomy infused teleoperation with application to brain computer interface controlled manipulation. Auton. Robots 41(6): 1401-1422 (2017) - [j16]Jiaji Zhou, Robert Paolini, Aaron M. Johnson, J. Andrew Bagnell, Matthew T. Mason:
A Probabilistic Planning Framework for Planar Grasping Under Uncertainty. IEEE Robotics Autom. Lett. 2(4): 2111-2118 (2017) - [c104]Hanzhang Hu, Wen Sun, Arun Venkatraman, Martial Hebert, J. Andrew Bagnell:
Gradient Boosting on Stochastic Data Streams. AISTATS 2017: 595-603 - [c103]Wen Sun, Arun Venkatraman, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell:
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction. ICML 2017: 3309-3318 - [c102]Arun Venkatraman, Nicholas Rhinehart, Wen Sun, Lerrel Pinto, Martial Hebert, Byron Boots, Kris M. Kitani, James Andrew Bagnell:
Predictive-State Decoders: Encoding the Future into Recurrent Networks. NIPS 2017: 1172-1183 - [c101]Jiaji Zhou, James A. Bagnell, Matthew T. Mason:
A Fast Stochastic Contact Model for Planar Pushing and Grasping: Theory and Experimental Validation. Robotics: Science and Systems 2017 - [r2]Jan Peters, J. Andrew Bagnell:
Policy Gradient Methods. Encyclopedia of Machine Learning and Data Mining 2017: 982-985 - [i38]Hanzhang Hu, Wen Sun, Arun Venkatraman, Martial Hebert, J. Andrew Bagnell:
Gradient Boosting on Stochastic Data Streams. CoRR abs/1703.00377 (2017) - [i37]Wen Sun, Arun Venkatraman, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell:
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction. CoRR abs/1703.01030 (2017) - [i36]Jiaji Zhou, J. Andrew Bagnell, Matthew T. Mason:
A Fast Stochastic Contact Model for Planar Pushing and Grasping: Theory and Experimental Validation. CoRR abs/1705.10664 (2017) - [i35]Shervin Javdani, Henny Admoni, Stefania Pellegrinelli, Siddhartha S. Srinivasa, J. Andrew Bagnell:
Shared Autonomy via Hindsight Optimization for Teleoperation and Teaming. CoRR abs/1706.00155 (2017) - [i34]Hanzhang Hu, Debadeepta Dey, J. Andrew Bagnell, Martial Hebert:
Anytime Neural Networks via Joint Optimization of Auxiliary Losses. CoRR abs/1708.06832 (2017) - [i33]Allison Del Giorno, J. Andrew Bagnell, Martial Hebert:
Ignoring Distractors in the Absence of Labels: Optimal Linear Projection to Remove False Positives During Anomaly Detection. CoRR abs/1709.04549 (2017) - [i32]Arun Venkatraman, Nicholas Rhinehart, Wen Sun, Lerrel Pinto, Martial Hebert, Byron Boots, Kris M. Kitani, J. Andrew Bagnell:
Predictive-State Decoders: Encoding the Future into Recurrent Networks. CoRR abs/1709.08520 (2017) - [i31]Hanzhang Hu, Debadeepta Dey, Allison Del Giorno, Martial Hebert, J. Andrew Bagnell:
Log-DenseNet: How to Sparsify a DenseNet. CoRR abs/1711.00002 (2017) - 2016
- [c100]Arun Venkatraman, Wen Sun, Martial Hebert, J. Andrew Bagnell, Byron Boots:
Online Instrumental Variable Regression with Applications to Online Linear System Identification. AAAI 2016: 2101-2107 - [c99]Allison Del Giorno, J. Andrew Bagnell, Martial Hebert:
A Discriminative Framework for Anomaly Detection in Large Videos. ECCV (5) 2016: 334-349 - [c98]Shervin Javdani, James Andrew Bagnell, Siddhartha S. Srinivasa:
Minimizing User Cost for Shared Autonomy. HRI 2016: 621-622 - [c97]Wen Sun, Arun Venkatraman, Byron Boots, J. Andrew Bagnell:
Learning to Filter with Predictive State Inference Machines. ICML 2016: 1197-1205 - [c96]Jiaji Zhou, Robert Paolini, J. Andrew Bagnell, Matthew T. Mason:
A convex polynomial force-motion model for planar sliding: Identification and application. ICRA 2016: 372-377 - [c95]Arun Venkatraman, Wen Sun, Martial Hebert, Byron Boots, J. Andrew Bagnell:
Inference Machines for Nonparametric Filter Learning. IJCAI 2016: 2074-2081 - [c94]Wen Sun, J. Andrew Bagnell:
Online Bellman Residual and Temporal Difference Algorithms with Predictive Error Guarantees. IJCAI 2016: 4213-4217 - [c93]Shreyansh Daftry, Sam Zeng, J. Andrew Bagnell, Martial Hebert:
Introspective perception: Learning to predict failures in vision systems. IROS 2016: 1743-1750 - [c92]Shreyansh Daftry, J. Andrew Bagnell, Martial Hebert:
Learning Transferable Policies for Monocular Reactive MAV Control. ISER 2016: 3-11 - [c91]Arun Venkatraman, Roberto Capobianco, Lerrel Pinto, Martial Hebert, Daniele Nardi, J. Andrew Bagnell:
Improved Learning of Dynamics Models for Control. ISER 2016: 703-713 - [c90]Hanzhang Hu, Alexander Grubb, J. Andrew Bagnell, Martial Hebert:
Efficient Feature Group Sequencing for Anytime Linear Prediction. UAI 2016 - [c89]Wen Sun, Roberto Capobianco, Geoffrey J. Gordon, J. Andrew Bagnell, Byron Boots:
Learning to Smooth with Bidirectional Predictive State Inference Machines. UAI 2016 - [p1]Jan Peters, Daniel D. Lee, Jens Kober, Duy Nguyen-Tuong, J. Andrew Bagnell, Stefan Schaal:
Robot Learning. Springer Handbook of Robotics, 2nd Ed. 2016: 357-398 - [i30]Jiaji Zhou, Robert Paolini, J. Andrew Bagnell, Matthew T. Mason:
A Convex Polynomial Force-Motion Model for Planar Sliding: Identification and Application. CoRR abs/1602.06056 (2016) - [i29]Shreyansh Daftry, Sam Zeng, Arbaaz Khan, Debadeepta Dey, Narek Melik-Barkhudarov, J. Andrew Bagnell, Martial Hebert:
Robust Monocular Flight in Cluttered Outdoor Environments. CoRR abs/1604.04779 (2016) - [i28]Shreyansh Daftry, Sam Zeng, J. Andrew Bagnell, Martial Hebert:
Introspective Perception: Learning to Predict Failures in Vision Systems. CoRR abs/1607.08665 (2016) - [i27]Shreyansh Daftry, J. Andrew Bagnell, Martial Hebert:
Learning Transferable Policies for Monocular Reactive MAV Control. CoRR abs/1608.00627 (2016) - [i26]Allison Del Giorno, J. Andrew Bagnell, Martial Hebert:
A Discriminative Framework for Anomaly Detection in Large Videos. CoRR abs/1609.08938 (2016) - 2015
- [j15]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:
CHIMP, the CMU Highly Intelligent Mobile Platform. J. Field Robotics 32(2): 209-228 (2015) - [c88]Abdeslam Boularias, James Andrew Bagnell, Anthony Stentz:
Learning to Manipulate Unknown Objects in Clutter by Reinforcement. AAAI 2015: 1336-1342 - [c87]Kevin Waugh, Dustin Morrill, James Andrew Bagnell, Michael H. Bowling:
Solving Games with Functional Regret Estimation. AAAI 2015: 2138-2145 - [c86]De-An Huang, Amir-massoud Farahmand, Kris M. Kitani, James Andrew Bagnell:
Approximate MaxEnt Inverse Optimal Control and Its Application for Mental Simulation of Human Interactions. AAAI 2015: 2673-2679 - [c85]Arun Venkatraman, Martial Hebert, J. Andrew Bagnell:
Improving Multi-Step Prediction of Learned Time Series Models. AAAI 2015: 3024-3030 - [c84]Yuxin Chen, Shervin Javdani, Amin Karbasi, J. Andrew Bagnell, Siddhartha S. Srinivasa, Andreas Krause:
Submodular Surrogates for Value of Information. AAAI 2015: 3511-3518 - [c83]Kevin Waugh, James Andrew Bagnell:
A Unified View of Large-Scale Zero-Sum Equilibrium Computation. AAAI Workshop: Computer Poker and Imperfect Information 2015 - [c82]Kevin Waugh, Dustin Morrill, James Andrew Bagnell, Michael Bowling:
Solving Games with Functional Regret Estimation. AAAI Workshop: Computer Poker and Imperfect Information 2015 - [c81]Debadeepta Dey, Kumar Shaurya Shankar, Sam Zeng, Rupesh Mehta, M. Talha Agcayazi, Christopher Eriksen, Shreyansh Daftry, Martial Hebert, J. Andrew Bagnell:
Vision and Learning for Deliberative Monocular Cluttered Flight. FSR 2015: 391-409 - [c80]Debadeepta Dey, Varun Ramakrishna, Martial Hebert, J. Andrew Bagnell:
Predicting Multiple Structured Visual Interpretations. ICCV 2015: 2947-2955 - [c79]Anca D. Dragan, Katharina Mülling, J. Andrew Bagnell, Siddhartha S. Srinivasa:
Movement primitives via optimization. ICRA 2015: 2339-2346 - [c78]Nicholas Rhinehart, Jiaji Zhou, Martial Hebert, J. Andrew Bagnell:
Visual chunking: A list prediction framework for region-based object detection. ICRA 2015: 5448-5454 - [c77]Sanjiban Choudhury, Sebastian A. Scherer, J. Andrew Bagnell:
Theoretical Limits of Speed and Resolution for Kinodynamic Planning in a Poisson Forest. Robotics: Science and Systems 2015 - [c76]Shervin Javdani, Siddhartha S. Srinivasa, J. Andrew Bagnell:
Shared Autonomy via Hindsight Optimization. Robotics: Science and Systems 2015 - [c75]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:
Autonomy Infused Teleoperation with Application to BCI Manipulation. Robotics: Science and Systems 2015 - [c74]Wen Sun, J. Andrew Bagnell:
Online Bellman Residual Algorithms with Predictive Error Guarantees. UAI 2015: 852-861 - [i25]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:
Autonomy Infused Teleoperation with Application to BCI Manipulation. CoRR abs/1503.05451 (2015) - [i24]Shervin Javdani, J. Andrew Bagnell, Siddhartha S. Srinivasa:
Shared Autonomy via Hindsight Optimization. CoRR abs/1503.07619 (2015) - [i23]Wen Sun, Arun Venkatraman, Byron Boots, J. Andrew Bagnell:
Learning to Filter with Predictive State Inference Machines. CoRR abs/1512.08836 (2015) - 2014
- [j14]Moslem Kazemi, Jean-Sebastien Valois, J. Andrew Bagnell, Nancy S. Pollard:
Human-inspired force compliant grasping primitives. Auton. Robots 37(2): 209-225 (2014) - [j13]Dov Katz, Arun Venkatraman, Moslem Kazemi, J. Andrew Bagnell, Anthony Stentz:
Perceiving, learning, and exploiting object affordances for autonomous pile manipulation. Auton. Robots 37(4): 369-382 (2014) - [c73]Abdeslam Boularias, James Andrew Bagnell, Anthony Stentz:
Efficient Optimization for Autonomous Robotic Manipulation of Natural Objects. AAAI 2014: 2520-2526 - [c72]Shervin Javdani, Yuxin Chen, Amin Karbasi, Andreas Krause, Drew Bagnell, Siddhartha S. Srinivasa:
Near Optimal Bayesian Active Learning for Decision Making. AISTATS 2014: 430-438 - [c71]Varun Ramakrishna, Daniel Munoz, Martial Hebert, James Andrew Bagnell, Yaser Sheikh:
Pose Machines: Articulated Pose Estimation via Inference Machines. ECCV (2) 2014: 33-47 - [i22]Shervin Javdani, Yuxin Chen, Amin Karbasi, Andreas Krause, J. Andrew Bagnell, Siddhartha S. Srinivasa:
Near Optimal Bayesian Active Learning for Decision Making. CoRR abs/1402.5886 (2014) - [i21]Stéphane Ross, J. Andrew Bagnell:
Reinforcement and Imitation Learning via Interactive No-Regret Learning. CoRR abs/1406.5979 (2014) - [i20]Hanzhang Hu, Alexander Grubb, J. Andrew Bagnell, Martial Hebert:
Efficient Feature Group Sequencing for Anytime Linear Prediction. CoRR abs/1409.5495 (2014) - [i19]Nicholas Rhinehart, Jiaji Zhou, Martial Hebert, J. Andrew Bagnell:
Visual Chunking: A List Prediction Framework for Region-Based Object Detection. CoRR abs/1410.7376 (2014) - [i18]Kevin Waugh, J. Andrew Bagnell:
A Unified View of Large-scale Zero-sum Equilibrium Computation. CoRR abs/1411.5007 (2014) - [i17]Debadeepta Dey, Kumar Shaurya Shankar, Sam Zeng, Rupesh Mehta, M. Talha Agcayazi, Christopher Eriksen, Shreyansh Daftry, Martial Hebert, J. Andrew Bagnell:
Vision and Learning for Deliberative Monocular Cluttered Flight. CoRR abs/1411.6326 (2014) - [i16]Kevin Waugh, Dustin Morrill, J. Andrew Bagnell, Michael Bowling:
Solving Games with Functional Regret Estimation. CoRR abs/1411.7974 (2014) - 2013
- [j12]Matthew Zucker, Nathan D. Ratliff, Anca D. Dragan, Mihail Pivtoraiko, Matthew Klingensmith, Christopher M. Dellin, J. Andrew Bagnell, Siddhartha S. Srinivasa:
CHOMP: Covariant Hamiltonian optimization for motion planning. Int. J. Robotics Res. 32(9-10): 1164-1193 (2013) - [j11]Jens Kober, J. Andrew Bagnell, Jan Peters:
Reinforcement learning in robotics: A survey. Int. J. Robotics Res. 32(11): 1238-1274 (2013) - [j10]Brian D. Ziebart, J. Andrew Bagnell, Anind K. Dey:
The Principle of Maximum Causal Entropy for Estimating Interacting Processes. IEEE Trans. Inf. Theory 59(4): 1966-1980 (2013) - [c70]Stéphane Ross, Jiaji Zhou, Yisong Yue, Debadeepta Dey, Drew Bagnell:
Learning Policies for Contextual Submodular Prediction. ICML (3) 2013: 1364-1372 - [c69]Ondrej Miksik, Daniel Munoz, J. Andrew Bagnell, Martial Hebert:
Efficient temporal consistency for streaming video scene analysis. ICRA 2013: 133-139 - [c68]Dov Katz, Moslem Kazemi, J. Andrew Bagnell, Anthony Stentz:
Clearing a pile of unknown objects using interactive perception. ICRA 2013: 154-161 - [c67]Stéphane Ross, Narek Melik-Barkhudarov, Kumar Shaurya Shankar, Andreas Wendel, Debadeepta Dey, J. Andrew Bagnell, Martial Hebert:
Learning monocular reactive UAV control in cluttered natural environments. ICRA 2013: 1765-1772 - [c66]Shervin Javdani, Matthew Klingensmith, J. Andrew Bagnell, Nancy S. Pollard, Siddhartha S. Srinivasa:
Efficient touch based localization through submodularity. ICRA 2013: 1828-1835 - [c65]Hanzhang Hu, Daniel Munoz, J. Andrew Bagnell, Martial Hebert:
Efficient 3-D scene analysis from streaming data. ICRA 2013: 2297-2304 - [c64]Dov Katz, Moslem Kazemi, J. Andrew Bagnell, Anthony Stentz:
Interactive segmentation, tracking, and kinematic modeling of unknown 3D articulated objects. ICRA 2013: 5003-5010 - [c63]Dov Katz, Arun Venkatraman, Moslem Kazemi, Drew Bagnell, Anthony Stentz:
Perceiving, Learning, and Exploiting Object Affordances for Autonomous Pile Manipulation. Robotics: Science and Systems 2013 - [i15]David M. Blei, J. Andrew Bagnell, Andrew McCallum:
Learning with Scope, with Application to Information Extraction and Classification. CoRR abs/1301.0556 (2013) - [i14]Stéphane Ross, Jiaji Zhou, Yisong Yue, Debadeepta Dey, J. Andrew Bagnell:
Learning Policies for Contextual Submodular Prediction. CoRR abs/1305.2532 (2013) - [i13]Kevin Waugh, Brian D. Ziebart, J. Andrew Bagnell:
Computational Rationalization: The Inverse Equilibrium Problem. CoRR abs/1308.3506 (2013) - [i12]Jiaji Zhou, Stéphane Ross, Yisong Yue, Debadeepta Dey, J. Andrew Bagnell:
Knapsack Constrained Contextual Submodular List Prediction with Application to Multi-document Summarization. CoRR abs/1308.3541 (2013) - [i11]Alexander Grubb, Daniel Munoz, J. Andrew Bagnell, Martial Hebert:
SpeedMachines: Anytime Structured Prediction. CoRR abs/1312.0579 (2013) - 2012
- [c62]Debadeepta Dey, Tian Yu Liu, Boris Sofman, James Andrew Bagnell:
Efficient Optimization of Control Libraries. AAAI 2012: 1983-1989 - [c61]Yuichi Ito, Kris M. Kitani, James A. Bagnell, Martial Hebert:
Detecting Interesting Events Using Unsupervised Density Ratio Estimation. ECCV Workshops (3) 2012: 151-161 - [c60]Kris M. Kitani, Brian D. Ziebart, James Andrew Bagnell, Martial Hebert:
Activity Forecasting. ECCV (4) 2012: 201-214 - [c59]Daniel Munoz, James Andrew Bagnell, Martial Hebert:
Co-inference for Multi-modal Scene Analysis. ECCV (6) 2012: 668-681 - [c58]Stéphane Ross, Drew Bagnell:
Agnostic System Identification for Model-Based Reinforcement Learning. ICML 2012 - [c57]David Silver, J. Andrew Bagnell, Anthony Stentz:
Active learning from demonstration for robust autonomous navigation. ICRA 2012: 200-207 - [c56]Matthew Zucker, J. Andrew Bagnell:
Reinforcement Planning: RL for optimal planners. ICRA 2012: 1850-1855 - [c55]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:
An integrated system for autonomous robotics manipulation. IROS 2012: 2955-2962 - [c54]David Silver, J. Andrew Bagnell, Anthony Stentz:
Learning Autonomous Driving Styles and Maneuvers from Expert Demonstration. ISER 2012: 371-386 - [c53]Brian D. Ziebart, Anind K. Dey, J. Andrew Bagnell:
Probabilistic pointing target prediction via inverse optimal control. IUI 2012: 1-10 - [c52]Paul Vernaza, Drew Bagnell:
Efficient high dimensional maximum entropy modeling via symmetric partition functions. NIPS 2012: 584-592 - [c51]Debadeepta Dey, Tian Yu Liu, Martial Hebert, J. Andrew Bagnell:
Contextual Sequence Prediction with Application to Control Library Optimization. Robotics: Science and Systems 2012 - [c50]Moslem Kazemi, Jean-Sebastien Valois, J. Andrew Bagnell, Nancy S. Pollard:
Robust Object Grasping using Force Compliant Motion Primitives. Robotics: Science and Systems 2012 - [c49]Alexander Grubb, Drew Bagnell:
SpeedBoost: Anytime Prediction with Uniform Near-Optimality. AISTATS 2012: 458-466 - [i10]Debadeepta Dey, Tian Yu Liu, Martial Hebert, J. Andrew Bagnell:
Predicting Contextual Sequences via Submodular Function Maximization. CoRR abs/1202.2112 (2012) - [i9]Stéphane Ross, J. Andrew Bagnell:
Agnostic System Identification for Model-Based Reinforcement Learning. CoRR abs/1203.1007 (2012) - [i8]David M. Bradley, J. Andrew Bagnell:
Convex Coding. CoRR abs/1205.2656 (2012) - [i7]Brian D. Ziebart, Anind K. Dey, J. Andrew Bagnell:
Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification. CoRR abs/1206.5281 (2012) - [i6]Shervin Javdani, Matthew Klingensmith, Drew Bagnell, Nancy S. Pollard, Siddhartha S. Srinivasa:
Efficient Touch Based Localization through Submodularity. CoRR abs/1208.6067 (2012) - [i5]Stéphane Ross, Narek Melik-Barkhudarov, Kumar Shaurya Shankar, Andreas Wendel, Debadeepta Dey, J. Andrew Bagnell, Martial Hebert:
Learning Monocular Reactive UAV Control in Cluttered Natural Environments. CoRR abs/1211.1690 (2012) - 2011
- [j9]Matthew Zucker, Nathan D. Ratliff, Martin Stolle, Joel E. Chestnutt, J. Andrew Bagnell, Christopher G. Atkeson, James Kuffner:
Optimization and learning for rough terrain legged locomotion. Int. J. Robotics Res. 30(2): 175-191 (2011) - [j8]Boris Sofman, Bradford Neuman, Anthony Stentz, J. Andrew Bagnell:
Anytime online novelty and change detection for mobile robots. J. Field Robotics 28(4): 589-618 (2011) - [c48]Brian D. Ziebart, J. Andrew Bagnell, Anind K. Dey:
Maximum causal entropy correlated equilibria for Markov games. AAMAS 2011: 207-214 - [c47]Stéphane Ross, Daniel Munoz, Martial Hebert, J. Andrew Bagnell:
Learning message-passing inference machines for structured prediction. CVPR 2011: 2737-2744 - [c46]Kevin Waugh, Brian D. Ziebart, Drew Bagnell:
Computational Rationalization: The Inverse Equilibrium Problem. ICML 2011: 1169-1176 - [c45]Alexander Grubb, Drew Bagnell:
Generalized Boosting Algorithms for Convex Optimization. ICML 2011: 1209-1216 - [c44]Xuehan Xiong, Daniel Munoz, J. Andrew Bagnell, Martial Hebert:
3-D scene analysis via sequenced predictions over points and regions. ICRA 2011: 2609-2616 - [c43]Bradford Neuman, Boris Sofman, Anthony Stentz, J. Andrew Bagnell:
Segmentation-based online change detection for mobile robots. ICRA 2011: 5427-5434 - [c42]Stéphane Ross, Geoffrey J. Gordon, Drew Bagnell:
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning. AISTATS 2011: 627-635 - [i4]Kevin Waugh, Brian D. Ziebart, J. Andrew Bagnell:
Computational Rationalization: The Inverse Equilibrium Problem. CoRR abs/1103.5254 (2011) - [i3]Alexander Grubb, J. Andrew Bagnell:
Generalized Boosting Algorithms for Convex Optimization. CoRR abs/1105.2054 (2011) - [i2]Stéphane Ross, J. Andrew Bagnell:
Stability Conditions for Online Learnability. CoRR abs/1108.3154 (2011) - 2010
- [j7]Raia Hadsell, J. Andrew Bagnell, Daniel F. Huber, Martial Hebert:
Space-carving Kernels for Accurate Rough Terrain Estimation. Int. J. Robotics Res. 29(8): 981-996 (2010) - [j6]David Silver, J. Andrew Bagnell, Anthony Stentz:
Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain. Int. J. Robotics Res. 29(12): 1565-1592 (2010) - [j5]J. Andrew Bagnell, David M. Bradley, David Silver, Boris Sofman, Anthony Stentz:
Learning for Autonomous Navigation. IEEE Robotics Autom. Mag. 17(2): 74-84 (2010) - [c41]Brian D. Ziebart, Drew Bagnell, Anind K. Dey:
Maximum Causal Entropy Correlated Equilibria for Markov Games. Interactive Decision Theory and Game Theory 2010 - [c40]Daniel Munoz, J. Andrew Bagnell, Martial Hebert:
Stacked Hierarchical Labeling. ECCV (6) 2010: 57-70 - [c39]Alexander Grubb, J. Andrew Bagnell:
Boosted Backpropagation Learning for Training Deep Modular Networks. ICML 2010: 407-414 - [c38]Brian D. Ziebart, J. Andrew Bagnell, Anind K. Dey:
Modeling Interaction via the Principle of Maximum Causal Entropy. ICML 2010: 1255-1262 - [c37]Boris Sofman, James A. Bagnell, Anthony Stentz:
Anytime online novelty detection for vehicle safeguarding. ICRA 2010: 1247-1254 - [c36]Matthew Zucker, James A. Bagnell, Christopher G. Atkeson, James Kuffner:
An optimization approach to rough terrain locomotion. ICRA 2010: 3589-3595 - [c35]Stéphane Ross, Drew Bagnell:
Efficient Reductions for Imitation Learning. AISTATS 2010: 661-668 - [r1]Jan Peters, J. Andrew Bagnell:
Policy Gradient Methods. Encyclopedia of Machine Learning 2010: 774-776 - [i1]Stéphane Ross, Geoffrey J. Gordon, J. Andrew Bagnell:
No-Regret Reductions for Imitation Learning and Structured Prediction. CoRR abs/1011.0686 (2010)
2000 – 2009
- 2009
- [j4]Nathan D. Ratliff, David Silver, J. Andrew Bagnell:
Learning to search: Functional gradient techniques for imitation learning. Auton. Robots 27(1): 25-53 (2009) - [c34]Brian D. Ziebart, Andrew L. Maas, J. Andrew Bagnell, Anind K. Dey:
Human Behavior Modeling with Maximum Entropy Inverse Optimal Control. AAAI Spring Symposium: Human Behavior Modeling 2009: 92- - [c33]Daniel Munoz, James A. Bagnell, Nicolas Vandapel, Martial Hebert:
Contextual classification with functional Max-Margin Markov Networks. CVPR 2009: 975-982 - [c32]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:
Autonomous Driving in Urban Environments: Boss and the Urban Challenge. The DARPA Urban Challenge 2009: 1-59 - [c31]Boris Sofman, J. Andrew Bagnell, Anthony Stentz:
Bandit-Based Online Candidate Selection for Adjustable Autonomy. FSR 2009: 239-248 - [c30]David Silver, J. Andrew Bagnell, Anthony Stentz:
Applied Imitation Learning for Autonomous Navigation in Complex Natural Terrain. FSR 2009: 249-259 - [c29]Nathan D. Ratliff, Matthew Zucker, J. Andrew Bagnell, Siddhartha S. Srinivasa:
CHOMP: Gradient optimization techniques for efficient motion planning. ICRA 2009: 489-494 - [c28]Garratt Gallagher, Siddhartha S. Srinivasa, J. Andrew Bagnell, Dave Ferguson:
GATMO: A Generalized Approach to Tracking Movable Objects. ICRA 2009: 2043-2048 - [c27]Brian D. Ziebart, Nathan D. Ratliff, Garratt Gallagher, Christoph Mertz, Kevin M. Peterson, James A. Bagnell, Martial Hebert, Anind K. Dey, Siddhartha S. Srinivasa:
Planning-based prediction for pedestrians. IROS 2009: 3931-3936 - [c26]David Silver, J. Andrew Bagnell, Anthony Stentz:
Perceptual Interpretation for Autonomous Navigation through Dynamic Imitation Learning. ISRR 2009: 433-449 - [c25]Raia Hadsell, J. Andrew Bagnell, Daniel F. Huber, Martial Hebert:
Accurate rough terrain estimation with space-carving kernels. Robotics: Science and Systems 2009 - [c24]David M. Bradley, J. Andrew Bagnell:
Convex Coding. UAI 2009: 83-90 - [c23]Nathan D. Ratliff, Brian D. Ziebart, Kevin M. Peterson, J. Andrew Bagnell, Martial Hebert, Anind K. Dey, Siddhartha S. Srinivasa:
Inverse Optimal Heuristic Control for Imitation Learning. AISTATS 2009: 424-431 - 2008
- [j3]J. Andrew Bagnell, Stefan Schaal:
Editorial: Special Issue on Machine Learning in Robotics. Int. J. Robotics Res. 27(2): 155-156 (2008) - [j2]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:
Autonomous driving in urban environments: Boss and the Urban Challenge. J. Field Robotics 25(8): 425-466 (2008) - [c22]Brian D. Ziebart, Andrew L. Maas, J. Andrew Bagnell, Anind K. Dey:
Maximum Entropy Inverse Reinforcement Learning. AAAI 2008: 1433-1438 - [c21]Brian D. Ziebart, Anind K. Dey, J. Andrew Bagnell:
Fast Planning for Dynamic Preferences. ICAPS 2008: 412-419 - [c20]Brian D. Ziebart, Andrew L. Maas, Anind K. Dey, J. Andrew Bagnell:
Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior. UbiComp 2008: 322-331 - [c19]Matthew Zucker, James Kuffner, James A. Bagnell:
Adaptive workspace biasing for sampling-based planners. ICRA 2008: 3757-3762 - [c18]J. Andrew Bagnell, David M. Bradley:
Differentiable Sparse Coding. NIPS 2008: 113-120 - [c17]David Silver, James A. Bagnell, Anthony Stentz:
High Performance Outdoor Navigation from Overhead Data using Imitation Learning. Robotics: Science and Systems 2008 - 2007
- [c16]Nathan D. Ratliff, James A. Bagnell, Siddhartha S. Srinivasa:
Imitation learning for locomotion and manipulation. Humanoids 2007: 392-397 - [c15]David M. Bradley, Ranjith Unnikrishnan, J. Andrew Bagnell:
Vegetation Detection for Driving in Complex Environments. ICRA 2007: 503-508 - [c14]Nathan D. Ratliff, J. Andrew Bagnell:
Kernel Conjugate Gradient for Fast Kernel Machines. IJCAI 2007: 1017-1022 - [c13]Brian D. Ziebart, Anind K. Dey, James A. Bagnell:
Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification. UAI 2007: 458-465 - [c12]Nathan D. Ratliff, J. Andrew Bagnell, Martin Zinkevich:
(Approximate) Subgradient Methods for Structured Prediction. AISTATS 2007: 380-387 - 2006
- [j1]Boris Sofman, Ellie Lin, J. Andrew Bagnell, John Cole, Nicolas Vandapel, Anthony Stentz:
Improving robot navigation through self-supervised online learning. J. Field Robotics 23(11-12): 1059-1075 (2006) - [c11]Nathan D. Ratliff, J. Andrew Bagnell, Martin Zinkevich:
Maximum margin planning. ICML 2006: 729-736 - [c10]David Silver, Boris Sofman, Nicolas Vandapel, J. Andrew Bagnell, Anthony Stentz:
Experimental Analysis of Overhead Data Processing To Support Long Range Navigation. IROS 2006: 2443-2450 - [c9]Nathan D. Ratliff, David M. Bradley, J. Andrew Bagnell, Joel E. Chestnutt:
Boosting Structured Prediction for Imitation Learning. NIPS 2006: 1153-1160 - [c8]Boris Sofman, Ellie Lin, J. Andrew Bagnell, Nicolas Vandapel, Anthony Stentz:
Improving Robot Navigation Through Self-Supervised Online Learning. Robotics: Science and Systems 2006 - 2005
- [c7]J. Andrew Bagnell:
Robust Supervised Learning. AAAI 2005: 714-719 - [c6]Jeff G. Schneider, David Apfelbaum, Drew Bagnell, Reid G. Simmons:
Learning Opportunity Costs in Multi-Robot Market Based Planners. ICRA 2005: 1151-1156 - [c5]J. Andrew Bagnell, Andrew Y. Ng:
On Local Rewards and Scaling Distributed Reinforcement Learning. NIPS 2005: 91-98 - 2003
- [c4]J. Andrew Bagnell, Jeff G. Schneider:
Covariant Policy Search. IJCAI 2003: 1019-1024 - [c3]J. Andrew Bagnell, Sham M. Kakade, Andrew Y. Ng, Jeff G. Schneider:
Policy Search by Dynamic Programming. NIPS 2003: 831-838 - 2002
- [c2]David M. Blei, J. Andrew Bagnell, Andrew Kachites McCallum:
Learning with Scope, with Application to Information Extraction and Classification. UAI 2002: 53-60 - 2001
- [c1]J. Andrew Bagnell, Jeff G. Schneider:
Autonomous Helicopter Control using Reinforcement Learning Policy Search Methods. ICRA 2001: 1615-1620
Coauthor Index
[c109] [j17] [c104] [c102] [i38] [i34] [i33] [i32] [i31] [c100] [c99] [c95] [c93] [c92] [c91] [c90] [i29] [i28] [i27] [i26] [c85] [c81] [c80] [c78] [c75] [i25] [c71] [i20] [i19] [i17] [c69] [c67] [c65] [i11] [c61] [c60] [c59] [c55] [c51] [i10] [i5] [c47] [c44] [j7] [c40] [c33] [c32] [c27] [c25] [c23] [j2]
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