Book chapters or sections

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• S. Gleason, M. Quigley, and P. Abbeel, "A GPS Software Receiver," in GNSS: Applications and Methods, GNSS Technology and Applications Series, Artech House, 2009, pp. 121-148.
• J. Z. Kolter, P. Abbeel, and A. Ng, "Hierarchical apprenticeship learning with application to quadruped locomotion," in Advances in Neural Information Processing Systems 20: Proc. 21st Annual Conf. (NIPS 2007), D. Koller, Y. Singer, and J. Platt, Eds., Advances in Neural Information Processing Systems, Vol. 20, Cambridge, MA: MIT Press, 2008, pp. 8 pg.
• G. Chechik, G. Heitz, G. Elidan, P. Abbeel, and D. Koller, "Max-margin classification of incomplete data," in Advances in Neural Information Processing Systems 19: Proc. 20th Annual Conf. (NIPS 2006), B. Scholkopf, J. Platt, and T. Hofmann, Eds., Advances in Neural Information Processing Systems, Vol. 19, Cambridge, MA: MIT Press, 2007, pp. 233-240.
• P. Abbeel, A. Coates, M. Quigley, and A. Y. Ng, "An application of reinforcement learning to aerobatic helicopter flight," in Advances in Neural Information Processing Systems 19: Proc. 20st Annual Conf. (NIPS 2006), B. Scholkopf, J. Platt, and T. Hofmann, Eds., Advances in Neural Informtion Processing Systems, Vol. 19, Cambridge, MA: MIT Press, 2007, pp. 1-8.
• P. Abbeel, V. Ganapathi, and A. Y. Ng, "Learning vehicular dynamics, with application to modeling helicopters," in Advances in Neural Information Processing Systems 18: Proc. 19th Annual Conf. (NIPS 2005), Y. Weiss, B. Scholkopf, and J. C. Platt, Eds., Advances in Neural Information Processing Systems, Vol. 18, Cambridge, MA: MIT Press, 2006, pp. 1-8.
• P. Abbeel, A. Coates, M. Montemerlo, A. Y. Ng, and S. Thrun, "Discriminative training of Kalman filters," in Robotics: Science and Systems I: Proc. 1st Conf. (RSS 2005), S. Thrun, G. Sukhatme, S. Schaal, and O. Brock, Eds., Cambridge, MA: MIT Press, 2005, pp. 289-296.
• P. Abbeel and A. Y. Ng, "Learning first-order Markov models for control," in Advances in Neural Information Processing Systems 17: Proc. 18th Annual Conf. (NIPS 2004), L. K. Saul, Y. Weiss, and L. Bottou, Eds., Advances in Neural Information Processing Systems, Vol. 17, Cambridge, MA: MIT Press, 2005, pp. 1-8.
• B. Taskar, M. F. Wong, P. Abbeel, and D. Koller, "Link prediction in relational data," in Advances in Neural Information Processing Systems 16: Proc. 17th Annual Conf. (NIPS 2003), S. Thrun, L. K. Saul, and B. Scholkopf, Eds., Advances in Neural Information Processing Systems, Vol. 16, Cambridge,MA: MIT Press, 2004, pp. 659-666.

Articles in journals or magazines

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• X. B. Peng, A. Kanazawa, J. Malik, P. Abbeel, and S. Levine, "SFV: Reinforcement Learning of Physical Skills from Videos," ACM Trans. Graph., vol. 37, no. 6, Nov. 2018.
• X. B. Peng, P. Abbeel, S. Levine, and M. van de Panne, "DeepMimic: Example-guided Deep Reinforcement Learning of Physics-based Character Skills," ACM Trans. Graph., vol. 37, no. 4, pp. 143:1--143:14, July 2018.
• B. Kehoe, S. Patil, P. Abbeel, and K. Goldberg, "A Survey of Research on Cloud Robotics and Automation," IEEE Trans. on Automation Science and Engineering: Special Issue on Cloud Robotics and Automation, vol. 12, no. 2, April 2015.
• P. Abbeel, A. Coates, and A. Y. Ng, "Autonomous Helicopter Aerobatics through Apprenticeship Learning," International Journal of Robotics Research (IJRR), June 2010.
• A. Coates, P. Abbeel, and A. Y. Ng, "Apprenticeship Learning for Helicopter Control," Communications of the ACM, July 2009.
• P. Abbeel, D. Koller, and A. Y. Ng, "Learning factor graphs in polynomial time and sample complexity," J. Machine Learning Research, vol. 7, pp. 1743-1788, Dec. 2006.

Articles in conference proceedings

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• X. Chen, S. Toyer, C. Wild, S. Emmons, I. Fischer, K. Lee, N. Alex, S. H. Wang, P. Luo, S. J. Russell, P. Abbeel, and R. Shah, "An Empirical Investigation of Representation Learning for Imitation," in Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2), 2021.
• A. Jain, P. Abbeel, and D. Pathak, "Locally Masked Convolution for Autoregressive Models," in Conference on Uncertainty in Artificial Intelligence (UAI), PMLR, 2020.
• J. Ho, A. Jain, and P. Abbeel, "Denoising Diffusion Probabilistic Models," in Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, Eds., Vol. 33, Curran Associates, Inc., 2020, pp. 6840--6851.
• S. Emmons, A. Jain, M. Laskin, T. Kurutach, P. Abbeel, and D. Pathak, "Sparse Graphical Memory for Robust Planning," in Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, Eds., Vol. 33, Curran Associates, Inc., 2020, pp. 5251--5262.
• P. Jain, A. Jain, A. Nrusimha, A. Gholami, P. Abbeel, K. Keutzer, I. Stoica, and J. Gonzalez, "Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization," in Proceedings of Machine Learning and Systems 2020, Machine Learning and Systems, 2020, pp. 497--511.
• R. Rao, N. Bhattacharya, N. Thomas, Y. Duan, P. Chen, J. F. Canny, P. Abbeel, and Y. S. Song, "Evaluating protein transfer learning with TAPE," Vol. 32, 2019.
• K. Hakhamaneshi, N. Werblun, P. Abbeel, and V. Stojanović, "BagNet: Berkeley Analog Generator with Layout Optimizer Boosted with Deep Neural Networks," in 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2019, pp. 1-8.
• X. B. Peng, A. Kanazawa, S. Toyer, P. Abbeel, and S. Levine, "Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse {RL}, and {GAN}s by Constraining Information Flow," in International Conference on Learning Representations, 2019.
• R. Rao, N. Bhattacharya, N. Thomas, Y. Duan, P. Chen, J. F. Canny, P. Abbeel, and Y. S. Song, "Evaluating Protein Transfer Learning with TAPE," in Advances in Neural Information Processing Systems (NeurIPS), Vol. 32, 2019.
• X. B. Peng, M. Andrychowicz, W. Zaremba, and P. Abbeel, "Sim-to-Real Transfer of Robotic Control with Dynamics Randomization," in 2018 IEEE International Conference on Robotics and Automation (ICRA), 2018, pp. 1-8.
• S. Krishnan, A. Garg, S. Patil, C. Lea, G. Hager, P. Abbeel, and K. Goldberg, "Transition State Clustering: Unsupervised Surgical Trajectory Segmentation For Robot Learning," in International Symposium on Robotics Research (ISRR), 2015.
• S. McKinley, A. Garg, S. Sen, R. Kapadia, A. Murali, K. Nichols, S. Lim, S. Patil, P. Abbeel, A. M. Okamura, and K. Goldberg, "A Disposable Haptic Palpation Probe for Locating Subcutaneous Blood Vessels in Robot-Assisted Minimally Invasive Surgery," in IEEE International Conference on Automation Science and Engineering (CASE), 2015.
• M. Laskey, J. Mahler, Z. McCarthy, F. T. Pokorny, S. Patil, J. Van Den Berg, D. Kragic, P. Abbeel, and K. Goldberg, "Multi-Arm Bandit Models for 2D Sample Based Grasp Planning with Uncertainty," in IEEE International Conference on Automation Science and Engineering (CASE), 2015.
• B. Charrow, G. Kahn, S. Patil, S. Liu, K. Goldberg, P. Abbeel, N. Michael, and V. Kumar, "Information-Theoretic Planning with Trajectory Optimization for Dense 3D Mapping," in Robotics: Science and Systems (RSS) Conference, 2015.
• A. Murali, S. Sen, B. Kehoe, A. Garg, S. McFarland, S. Patil, W. D. Boyd, S. Lim, P. Abbeel, and K. Goldberg, "Learning by Observation for Surgical Subtasks: Multilateral Cutting of 3D Viscoelastic and 2D Orthotropic Tissue Phantoms," 2015.
• J. Mahler, S. Patil, B. Kehoe, J. Van Den Berg, M. Ciocarlie, P. Abbeel, and K. Goldberg, "GP-GPIS-OPT: Grasp Planning Under Shape Uncertainty Using Gaussian Process Implicit Surfaces and Sequential Convex Programming," in IEEE International Conference on Robotics and Automation, 2015.
• A. Murali, S. Sen, B. Kehoe, A. Garg, S. McFarland, S. Patil, W. D. Boyd, S. Lim, P. Abbeel, and K. Goldberg, "Learning by Observation for Surgical Subtasks: Multilateral Cutting of 3D Viscoelastic and 2D Orthotropic Tissue Phantoms," in IEEE International Conference on Robotics and Automation, 2015.
• L. Jaillet, J. Hoffman, J. van den Berg, P. Abbeel, J. M. Porta, and K. Goldberg, "EG-RRT: Environment-Guided Random Trees for Kinodynamic Motion Planning with Uncertainty and Obstacles," in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2011.
• S. Javdani, S. Tandon, J. Tang, J. O'Brien, and P. Abbeel, "Modeling and Perception of Deformable One-Dimensional Objects," in Proceedings of IEEE International Conference on Robotics and Automation (ICRA) 2011, 2011, pp. 1-8.
• M. Cusumano-Towner, A. Singh, S. Miller, J. O'Brien, and P. Abbeel, "Bringing Clothing into Desired Configurations with Limited Perception," in Proceedings of IEEE International Conference on Robotics and Automation (ICRA) 2011, 2011, pp. 1-8.
• J. van den Berg, P. Abbeel, and K. Goldberg, "LQG-MP: Optimized Path Planning for Robots with Motion Uncertainty and Imperfect State Information," in Proceedings of Robotics: Science and Systems (RSS), 2010.
• J. Maitin-Shepard, M. Cusumano-Towner, J. Lei, and P. Abbeel, "Cloth Grasp Point Detection based on Multiple-View Geometric Cues with Application to Robotic Towel Folding," in Proceedings 2010 Conference on Robotics and Automation (ICRA), 2010.
• J. Tang, A. Singh, N. Goehausen, and P. Abbeel, "Learning Parameterized Maneuvers for Autonomous Helicopter Flight," in Proceedings 2010 Conference on Robotics and Automation (ICRA), 2010.
• J. van den Berg, S. Miller, D. Duckworth, H. Hu, X. Fu, K. Goldberg, and P. Abbeel, "Superhuman Performance of Surgical Tasks by Robots using Iterative Learning from Human-Guided Demonstrations (Best Medical Robotics Paper Award)," in Proceedings 2010 Conference on Robotics and Automation (ICRA), 2010.
• P. J. From, J. T. Gravdahl, and P. Abbeel, "On the Influence of Ship Motion Prediction Accuracy on Motion Planning and Control of Robotic Manipulators on Seaborne Platforms," in Proceedings 2010 Conference on Robotics and Automation (ICRA), 2010.
• S. Gleason, M. Quigley, and P. Abbeel, "An Open Source AGPS/DGPS Capable C-coded Software Receiver," in Proceedings of the 22nd International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS 2009), 2009, pp. 1926 - 1931.
• P. Abbeel, D. Dolgov, A. Y. Ng, and S. Thrun, "Apprenticeship learning for motion planning with application to parking lot navigation," in Proc. 2008 IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS '08), Piscataway, NJ: IEEE Press, 2008, pp. 1083-1090.
• A. Coates, P. Abbeel, and A. Y. Ng, "Learning for control from multiple demonstrations (Best Application Paper Award)," in Proc. 25th Intl. Conf. on Machine Learning (ICML 2008), A. McCallum and S. Roweis, Eds., ACM International Conference Proceeding Series, Vol. 307, New York, NY: The Association for Computing Machinery, Inc., 2008, pp. 144-151.
• P. Abbeel, A. coates, T. Hunter, and A. Y. Ng, "Autonomous autorotation of an RC helicopter (Ben Wegbreit IFRR Student Fellowship Award)," in Proc. 11th Intl. Symp. on Experimental Robotics (ISER 2008), Springer Tracts for Advanced Robotics, Berlin, Germany: Springer-Verlag, 2008, pp. 14 pg.
• M. Quigley, P. Abbeel, D. S. De Lorenzo, Y. Gu, S. Bolouki, D. Akos, and A. Y. Ng, "Portable GNSS baseband logging," in Proc. 20th Intl. Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2007), Red Hook, NY: Curran Associates, Inc., 2008, pp. 2216-2223.
• S. I. Lee, H. Lee, P. Abbeel, and A. Y. Ng, "Efficient L1 regularized logistic regression," in Proc. 21st National Conf. on Artificial Intelligence (AAAI-06), Menlo Park, CA: AAAI Press, 2006, pp. 401-407.
• P. Abbeel, M. Quigley, and A. Y. Ng, "Using inaccurate models in reinforcement learning," in Proc. 23 Intl. Conf. on Machine Learning (ICML 2006), W. W. Cohen and A. Moore, Eds., ACM International Conference Proceedings Series, Vol. 148, New York, NY: The Association for Computing Machinery, Inc., 2006, pp. 1-8.
• P. Abbeel and A. Y. Ng, "Exploration and apprenticeship learning in reinforcement learning," in Proc. 22nd Intl. Conf. on Machine Learning (ICML 2005), L. De Raedt and S. Wrobel, Eds., ACM International Conference Proceeding Series, Vol. 119, New York, NY: The Association for Computing Machinery, Inc., 2005, pp. 1-8.
• P. Abbeel, D. Koller, and A. Y. Ng, "Learning factor graphs in polynomial time and sample complexity," in Proc. 21st Conf. on Uncertainty in Artificial Intelligence (UAI 2005), Arlington, VA: AUAI Press, 2005, pp. 1-9.
• P. Abbeel and A. Y. Ng, "Apprenticeship learning via inverse reinforcement learning," in Proc. 21st Intl. Conf. on Machine Learning (ICML 2004), C. Brodley, Ed., ACM International Conference Proceeding Series, Vol. 69, New York, NY: The Association for Computing Machinery, Inc., 2004, pp. 8 pg.
• B. Taskar, P. Abbeel, and D. Koller, "Discriminative probabilistic models for relational data," in Proc. 18th Annual Conf. on Uncertainty in Artificial Intelligence (UAI-02), A. Darwiche and N. Friedman, Eds., San Francisco, CA: Morgan Kaufmann Publishers, 2002, pp. 485-492.

Technical Reports

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• I. Stoica, D. Song, R. A. Popa, D. A. Patterson, M. W. Mahoney, R. H. Katz, A. D. Joseph, M. Jordan, J. M. Hellerstein, J. Gonzalez, K. Goldberg, A. Ghodsi, D. E. Culler, and P. Abbeel, "A Berkeley View of Systems Challenges for AI," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2017-159, Oct. 2017.
• K. Narayan, P. Abbeel, J. Malik, A. Efros, and M. Banks, "Scalable High-Quality 3D Scanning," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2016-220, Dec. 2016.

Ph.D. Theses

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• A. Adeniji, "Toward Robots that Learn from Everyday Human Experience," P. Abbeel, S. Levine, and K. Goldberg, Eds., EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2025-200, Dec. 2025.
• Y. Liu and P. Abbeel, "Perception for Real-World Robotic Applications," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2023-122, May 2023.
• A. Akametalu, "A Learning-Based Approach to Safety for Uncertain Robotic Systems," C. Tomlin, P. Abbeel, and L. Evans, Eds., EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2018-41, May 2018.
• T. Moldovan, "Safety, Risk Awareness and Exploration in Reinforcement Learning," P. Abbeel, M. Jordan, and F. Borrelli, Eds., EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2016-20, May 2016.
• P. Abbeel, "Apprenticeship Learning and Reinforcement Learning with Application to Robotic Control," Stanford University, Department of Computer Science, Aug. 2008.

Masters Reports

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• A. Jain and P. Abbeel, "Point Track Prediction Models Enable Imitation from Action-less Datasets," 2025.
• V. Lee, M. Nguyen, L. Elzeiny, and C. Deng, "Chip Placement with Diffusion Models," P. Abbeel and J. Wawrzynek, Eds., EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2025-183, 2025.
• C. Gordon, A. Lu, and P. Abbeel, "Protein Language Model Fitness is a Matter of Preference," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2025-116, May 2025.
• Y. Shentu, P. Wu, A. Rajeswaran, and P. Abbeel, "From LLMs to Actions: Latent Codes as Bridges in Hierarchical Robot Control," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2024-217, Dec. 2024.
• A. Xie, A. Jain, and P. Abbeel, "VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2023-61, May 2023.
• M. Zhao, P. Abbeel, and S. James, "On the E􏰀ectiveness of Fine Tuning versus Meta-reinforcement Learning," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2022-202, Aug. 2022.
• R. Zhao, K. Lu, P. Abbeel, and S. Tiomkin, "Efficient Empowerment Estimation for Unsupervised Stabilization," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2021-109, May 2021.
• P. Jain, A. Jain, T. Zhang, P. Abbeel, J. Gonzalez, and I. Stoica, "Learning Self-Supervised Representations of Code Functionality," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2021-62, May 2021.
• K. Parvate, "On Training Robust Policies for Flow Smoothing," A. Bayen and P. Abbeel, Eds., EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2020-197, Dec. 2020.
• T. Zhang, Z. McCarthy, O. Jow, D. Lee, X. Chen, K. Goldberg, and P. Abbeel, "Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2020-190, Dec. 2020.
• A. Li, "Algorithms for Multi-task Reinforcement Learning," P. Abbeel, Ed., EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2020-110, May 2020.
• R. Hoque, "Robotic Fabric Manipulation with Deep Imitation Learning and Reinforcement Learning in Simulation," K. Goldberg and P. Abbeel, Eds., EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2020-72, May 2020.
• X. Lu, S. Tiomkin, and P. Abbeel, "Generalization via Information Bottleneck in Deep Reinforcement Learning," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2020-56, May 2020.
• J. Luo, "Reinforcement Learning for Robotic Assembly with Force Control," P. Abbeel, Ed., EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2020-20, Feb. 2020.
• C. Florensa Campo, D. Held, M. Wulfmeier, M. Zhang, and P. Abbeel, "Reverse Curriculum Generation for Reinforcement Learning," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2018-162, Dec. 2018.
• G. Kahn, A. Villaflor, B. Ding, P. Abbeel, and S. Levine, "Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2018-47, May 2018.
• Y. Liu, A. Gupta, P. Abbeel, and S. Levine, "Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2018-37, May 2018.
• N. Mishra, M. Rohaninejad, X. Chen, and P. Abbeel, "A Simple Neural Attentive Meta-Learner," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2018-32, May 2018.
• A. Garg, "Autonomous Palpation for Tumor Localization: Design of a Palpation Probe and Gaussian Process Adaptive Sampling," K. Goldberg, P. Abbeel, and A. Atamturk, Eds., EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2016-140, Aug. 2016.
• W. Han, S. Levine, and P. Abbeel, "Learning Compound Multi-Step Controllers under Unknown Dynamics," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2016-41, May 2016.
• B. Shahsavari and P. Abbeel, "Short-Term Traffic Forecasting: Modeling and Learning Spatio-Temporal Relations in Transportation Networks Using Graph Neural Networks," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2015-243, Dec. 2015.
• A. Punjani and P. Abbeel, "Machine Learning for Helicopter Dynamics Models," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2014-219, Dec. 2014.
• M. Tayson-Frederick, "Reinforcement Learning Methods to Enable Automatic Tuning of Legged Robots," P. Abbeel and R. S. Fearing, Eds., EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2012-145, May 2012.
• N. Zeitlin, "Reinforcement Learning Methods to Enable Automatic Tuning of Legged Robots," P. Abbeel and R. S. Fearing, Eds., EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2012-129, May 2012.
• A. Janoch, "The Berkeley 3D Object Dataset," T. Darrell, P. Abbeel, and J. Malik, Eds., EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2012-85, May 2012.