Abbeel, Pieter

An algorithmic perspective on imitation learning

Osa, T., Pajarinen, J., Neumann, G., Bagnell, J. A., Abbeel, P., & Peters, J. (2018) · Foundations and Trends in Robotics

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 405

Learning to Adapt in Dynamic, Real-World Environments through Meta-Reinforcement Learning

Nagabandi, A., Clavera, I., Liu, S., Fearing, R. S., Abbeel, P., Levine, S., & Finn, C. (2018) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 370

MSA Transformer

Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J. F., Abbeel, P., Sercu, T., & Rives, A. (2021) · bioRxiv (Cold Spring Harbor Laboratory)

Machine Learning in BioinformaticsProtein Structure and Dynamics

Cited by 350

Managing extreme AI risks amid rapid progress

Bengio, Y., Hinton, G., Yao, A., Song, D., Abbeel, P., Darrell, T., Harari, Y. N., Zhang, Y.-Q., Xue, L., Shalev-Shwartz, S., et al. (2024) · Science

Ethics and Social Impacts of AIDigitalization, Law, and Regulation

Cited by 349

Learning Interactive Real-World Simulators

Yang, S., Du, Y., Ghasemipour, K., Tompson, J., Kaelbling, L., Schuurmans, D., & Abbeel, P. (2023) · arXiv

Reinforcement Learning in RoboticsMultimodal Machine Learning Applications

Cited by 346

One-Shot Visual Imitation Learning via Meta-Learning

Finn, C., Yu, T., Zhang, T., Abbeel, P., & Levine, S. (2017) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 341

Self-Supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation

Kahn, G., Villaflor, A., Ding, B., Abbeel, P., & Levine, S. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA)

Reinforcement Learning in RoboticsRobotics and Sensor-Based Localization

Cited by 308

Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents

Huang, W., Abbeel, P., Pathak, D., & Mordatch, I. (2022) · arXiv

AI-based Problem Solving and PlanningRobot Manipulation and Learning

Cited by 304

Reinforcement Learning with Augmented Data

Laskin, M., Lee, K., Stooke, A., Pinto, L., Abbeel, P., & Srinivas, A. (2020) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 299

Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design

Ho, J., Chen, X., Srinivas, A., Duan, Y., & Abbeel, P. (2019) · arXiv

Generative Adversarial Networks and Image SynthesisAdversarial Robustness in Machine Learning

Cited by 245

Benchmarking Model-Based Reinforcement Learning

Wang, T., Bao, X., Clavera, I., Hoang, J., Wen, Y., Langlois, E., Zhang, S., Zhang, G., Abbeel, P., & Ba, J. (2019) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 226

Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots

Li, Z., Cheng, X., Peng, X. B., Abbeel, P., Levine, S., Berseth, G., & Sreenath, K. (2021) · 2021 IEEE International Conference on Robotics and Automation (ICRA)

Robotic Locomotion and ControlReinforcement Learning in Robotics

Cited by 219

Learning with Opponent-Learning Awareness

Foerster, J., Chen, R. Y., Al-Shedivat, M., Whiteson, S., Abbeel, P., & Mordatch, I. (2018) · International Joint Conference on Autonomous Agents and Multiagent Systems

Reinforcement Learning in RoboticsGame Theory and Applications

Cited by 215

Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration 0

O’Neill, A., Rehman, A., Maddukuri, A., Gupta, A., Padalkar, A., Lee, A., Pooley, A., Gupta, A., Mandlekar, A., Jain, A., et al. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA)

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 210

A Berkeley View of Systems Challenges for AI

Stoica, I., Song, D., Popa, R. A., Patterson, D., Mahoney, M. W., Katz, R., Joseph, A. D., Jordan, M., Hellerstein, J. M., Gonzalez, J. E., et al. (2017) · arXiv

Artificial Intelligence ApplicationsAdvanced Neural Network Applications

Cited by 171

On the Utility of Learning about Humans for Human-AI Coordination

Carroll, M., Shah, R., Ho, M. K., Griffiths, T. L., Seshia, S. A., Abbeel, P., & Dragan, A. (2019) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 168

Stochastic latent actor–critic: Deep reinforcement learning with a latent variable model

Lee, A. X., Nagabandi, A., Abbeel, P., & Levine, S. (2019) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 159

VideoGPT: Video Generation using VQ-VAE and Transformers

Yan, W., Zhang, Y., Abbeel, P., & Srinivas, A. (2021) · arXiv

Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning Applications

Cited by 156

Safe Exploration in Markov Decision Processes

Moldovan, T. M., & Abbeel, P. (2012) · arXiv

Reinforcement Learning in RoboticsAdvanced Bandit Algorithms Research

Cited by 152

Learning force-based manipulation of deformable objects from multiple demonstrations

Lee, A. X., Lu, H., Gupta, A., Levine, S., & Abbeel, P. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA)

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 140

Learning from Demonstrations Through the Use of Non-rigid Registration

Schulman, J., Ho, J., Lee, C., & Abbeel, P. (2016) · Springer Tracts in Advanced Robotics

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 133

Contrastive Code Representation Learning

Jain, P., Jain, A., Zhang, T., Abbeel, P., Gonzalez, J. E., & Stoica, I. (2020) · arXiv

Software Engineering ResearchTopic Modeling

Cited by 129

Planning to Explore via Self-Supervised World Models

Sekar, R., Rybkin, O., Daniilidis, K., Abbeel, P., Hafner, D., & Pathak, D. (2020) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 129

Contrastive Code Representation Learning

Jain, P., Jain, A., Zhang, T., Abbeel, P., Gonzalez, J., & Stoica, I. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Software Engineering ResearchTopic Modeling

Cited by 129