Pinto, Lerrel

Robust adversarial reinforcement learning

Pinto, L., Davidson, J., Sukthankar, R., & Gupta, A. (2017) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 507

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

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

Learning Predictive Representations for Deformable Objects Using Contrastive Estimation

Yan, W., Vangipuram, A., Abbeel, P., & Pinto, L. (2020) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 122

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

Embodiment Collaboration, O'Neill, A., Rehman, A., Gupta, A., Maddukuri, A., Gupta, A., Padalkar, A., Lee, A., Pooley, A., Gupta, A., et al. (2023) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 111

The Surprising Effectiveness of Representation Learning for Visual Imitation

Pari, J., Shafiullah, N. M. (., Arunachalam, S. P., & Pinto, L. (2022) · Robotics: Science and Systems XVIII

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 95

The Surprising Effectiveness of Representation Learning for Visual Imitation

Pari, J., Shafiullah, N. M., Arunachalam, S. P., & Pinto, L. (2021) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 94

Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning

Yarats, D., Fergus, R., Lazaric, A., & Pinto, L. (2021) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 89

Holo-Dex: Teaching Dexterity with Immersive Mixed Reality

Arunachalam, S. P., Güzey, I., Chintala, S., & Pinto, L. (2022) · arXiv

Robot Manipulation and LearningTeleoperation and Haptic Systems

Cited by 48

Visual imitation made easy

Young, S., Gandhi, D., Tulsiani, S., Gupta, A., Abbeel, P., & Pinto, L. (2020) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 42

Learning Visual Robotic Control Efficiently with Contrastive Pre-training and Data Augmentation

Zhan, A., Zhao, R., Pinto, L., Abbeel, P., & Laskin, M. (2020) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 28

See to touch: Learning tactile dexterity through visual incentives

Guzey, I., Dai, Y., Evans, B., Chintala, S., & Pinto, L. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA)

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 26

Behavior Generation with Latent Actions

Lee, S., Wang, Y., Etukuru, H., Kim, H. J., Shafiullah, N. M. M., & Pinto, L. (2024) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 7

DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning

Zhou, G., Pan, H., LeCun, Y., & Pinto, L. (2024) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 5

BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games

Paglieri, D., Cupiał, B., Coward, S., Piterbarg, U., Wolczyk, M., Khan, A., Pignatelli, E., Kuciński, Ł., Pinto, L., Fergus, R., et al. (2024) · arXiv

Artificial Intelligence in GamesMultimodal Machine Learning Applications

Cited by 2

diff History for Neural Language Agents

Piterbarg, U., Pinto, L., & Fergus, R. (2023) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 0