Tan, Jie

Soft Actor-Critic Algorithms and Applications

Haarnoja, T., Zhou, A., Hartikainen, K., Tucker, G., Ha, S., Tan, J., Kumar, V., Zhu, H., Gupta, A., Abbeel, P., et al. (2018) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 1,994

Sim-to-Real: Learning Agile Locomotion For Quadruped Robots

Tan, J., Zhang, T., Coumans, E., Iscen, A., Bai, Y., Hafner, D., Bohez, S., & Vanhoucke, V. (2018) · Robotics: Science and Systems XIV

Robotic Locomotion and ControlReinforcement Learning in Robotics

Cited by 735

Sim-to-Real: Learning Agile Locomotion For Quadruped Robots

Tan, J., Zhang, T., Coumans, E., Iscen, A., Bai, Y., Hafner, D., Bohez, S., & Vanhoucke, V. (2018) · arXiv

Robotic Locomotion and ControlReinforcement Learning in Robotics

Cited by 734

How to train your robot with deep reinforcement learning: Lessons we have learned

Ibarz, J., Tan, J., Finn, C., Kalakrishnan, M., Pastor, P., & Levine, S. (2021) · The International Journal of Robotics Research

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 570

Learning Agile Robotic Locomotion Skills by Imitating Animals

Bin Peng, X., Coumans, E., Zhang, T., Lee, T.-W., Tan, J., & Levine, S. (2020) · Robotics: Science and Systems XVI

Robotic Locomotion and ControlReinforcement Learning in Robotics

Cited by 357

Preparing for the unknown: Learning a universal policy with online system identification

Yu, W., Tan, J., Karen Liu, C., & Turk, G. (2017) · Robotics: Science and Systems XIII

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 237

Preparing for the Unknown: Learning a Universal Policy with Online System Identification

Yu, W., Tan, J., Liu, C. K., & Turk, G. (2017) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 237

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

On the use of simulation in robotics: Opportunities, challenges, and suggestions for moving forward

Choi, H., Crump, C., Duriez, C., Elmquist, A., Hager, G., Han, D., Hearl, F., Hodgins, J., Jain, A., Leve, F., et al. (2021) · Proceedings of the National Academy of Sciences

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 162

Learning to dress: Synthesizing human dressing motion via deep reinforcement learning

Clegg, A., Yu, W., Tan, J., Liu, C. K., & Turk, G. (2018) · ACM Transactions on Graphics

Reinforcement Learning in RoboticsHuman Motion and Animation

Cited by 89

Learning to Walk via Deep Reinforcement Learning

Haarnoja, T., Ha, S., Zhou, A., Tan, J., Tucker, G., & Levine, S. (2018) · arXiv

Reinforcement Learning in RoboticsRobotic Locomotion and Control

Cited by 53

Haptic simulation for robot-assisted dressing

Yu, W., Kapusta, A., Tan, J., Kemp, C. C., Turk, G., & Liu, C. K. (2017) · 2017 IEEE International Conference on Robotics and Automation (ICRA)

Robot Manipulation and LearningProsthetics and Rehabilitation Robotics

Cited by 41

Learning to navigate cloth using haptics

Clegg, A., Yu, W., Erickson, Z., Tan, J., Liu, C. K., & Turk, G. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 20