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J. 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]