IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data
暂无分享,去创建一个
[1] Emma Brunskill,et al. Off-Policy Policy Gradient with State Distribution Correction , 2019, UAI 2019.
[2] Sergey Levine,et al. Learning Hand-Eye Coordination for Robotic Grasping with Large-Scale Data Collection , 2016, ISER.
[3] Sergey Levine,et al. Learning Latent Plans from Play , 2019, CoRL.
[4] Abhinav Gupta,et al. Supersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours , 2015, 2016 IEEE International Conference on Robotics and Automation (ICRA).
[5] Marcin Andrychowicz,et al. Hindsight Experience Replay , 2017, NIPS.
[6] Aysegul Dundar,et al. Unsupervised Disentanglement of Pose, Appearance and Background from Images and Videos , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[7] John B. Shoven,et al. I , Edinburgh Medical and Surgical Journal.
[8] Dale Schuurmans,et al. Striving for Simplicity in Off-policy Deep Reinforcement Learning , 2019, ArXiv.
[9] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[10] Pieter Abbeel,et al. Goal-conditioned Imitation Learning , 2019, NeurIPS.
[11] Silvio Savarese,et al. ROBOTURK: A Crowdsourcing Platform for Robotic Skill Learning through Imitation , 2018, CoRL.
[12] Nando de Freitas,et al. Reinforcement and Imitation Learning for Diverse Visuomotor Skills , 2018, Robotics: Science and Systems.
[13] Sergey Levine,et al. QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation , 2018, CoRL.
[14] Doina Precup,et al. Off-Policy Deep Reinforcement Learning without Exploration , 2018, ICML.
[15] Kuan-Ting Yu,et al. More than a million ways to be pushed. A high-fidelity experimental dataset of planar pushing , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[16] Dean Pomerleau,et al. ALVINN, an autonomous land vehicle in a neural network , 2015 .
[17] Xinyu Liu,et al. Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics , 2017, Robotics: Science and Systems.
[18] Brijen Thananjeyan,et al. SWIRL: A sequential windowed inverse reinforcement learning algorithm for robot tasks with delayed rewards , 2018, Int. J. Robotics Res..
[19] Martial Hebert,et al. Learning monocular reactive UAV control in cluttered natural environments , 2012, 2013 IEEE International Conference on Robotics and Automation.
[20] Marcin Andrychowicz,et al. Overcoming Exploration in Reinforcement Learning with Demonstrations , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[21] Sergey Levine,et al. Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction , 2019, NeurIPS.
[22] Matei T. Ciocarlie,et al. The Columbia grasp database , 2009, 2009 IEEE International Conference on Robotics and Automation.
[23] Natasha Jaques,et al. Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog , 2019, ArXiv.
[24] J. Andrew Bagnell,et al. Reinforcement and Imitation Learning via Interactive No-Regret Learning , 2014, ArXiv.
[25] Yang Gao,et al. Reinforcement Learning from Imperfect Demonstrations , 2018, ICLR.
[26] A. Fischer. Inverse Reinforcement Learning , 2012 .
[27] Pieter Abbeel,et al. Learning from Demonstrations Through the Use of Non-rigid Registration , 2013, ISRR.
[28] Thomas Brox,et al. CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity , 2019, 1902.05605.
[29] Sergey Levine,et al. Temporal Difference Models: Model-Free Deep RL for Model-Based Control , 2018, ICLR.
[30] Silvio Savarese,et al. SURREAL: Open-Source Reinforcement Learning Framework and Robot Manipulation Benchmark , 2018, CoRL.
[31] Silvio Savarese,et al. Learning task-oriented grasping for tool manipulation from simulated self-supervision , 2018, Robotics: Science and Systems.
[32] Rüdiger Dillmann,et al. The KIT object models database: An object model database for object recognition, localization and manipulation in service robotics , 2012, Int. J. Robotics Res..
[33] Christopher Burgess,et al. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework , 2016, ICLR 2016.
[34] Sergey Levine,et al. Data-Efficient Hierarchical Reinforcement Learning , 2018, NeurIPS.
[35] Martin A. Riedmiller,et al. Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards , 2017, ArXiv.
[36] Percy Liang,et al. Know What You Don’t Know: Unanswerable Questions for SQuAD , 2018, ACL.
[37] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[38] Pieter Abbeel,et al. Towards Characterizing Divergence in Deep Q-Learning , 2019, ArXiv.