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With the rapid growth of computing powers and recent advances in deep learning, we have witnessed impressive demonstrations of novel robot capabilities in research settings.
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“QT-Opt: Scalable Deep Reinforcement Learning for Vision-based Robotic Manipulation”
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“Shared Autonomy via Deep Reinforcement Learning”
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“Deep Tamer: Interactive Agent Shaping in High-Dimensional State Spaces”
Garrett Warnell, Nicholas Waytowich, Vernon Lawhern and Peter Stone · 2018
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Tianhao Zhang et al · 2018
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“Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations”
Daniel. Brown, Wonjoon Goo, Prabhat Nagarajan and Scott Niekum · 2019
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“Scaling Data-driven Robotics With Reward Sketching and Batch Reinforcement Learning”
Serkan Cabi et al · 2019
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“Off-policy Deep Reinforcement Learning Without Exploration”
Scott Fujimoto, David Meger and Doina Precup · 2019
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“HG-DAgger: Interactive Imitation Learning with Human Experts”
Michael Kelly, Chelsea Sidrane, Katherine Driggs-Campbell and Mykel Kochenderfer · 2019
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“EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning”
Kunal Menda, Katherine Driggs-Campbell and Mykel. Kochenderfer · 2019
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“robosuite: A Modular Simulation Framework and Benchmark for Robot Learning”
Yuke Zhu, Josiah Wong, Ajay Mandlekar and Roberto Martı́n-Martı́n · 2020
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“Offline Learning from Demonstrations and Unlabeled Experience”
Konrad Zolna et al · 2020
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“Opal: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning”
Anurag Ajay et al · 2021
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“Correct Me If I Am Wrong: Interactive Learning for Robotic Manipulation”
Eugenio Chisari et al · 2021
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“Understanding the Relationship between Interactions and Outcomes in Human-in-the-Loop Machine Learning”
Yuchen Cui et al · 2021
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“Implicit Behavioral Cloning”
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Ruohan Zhang et al · 2019
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“A Survey on Interactive Reinforcement Learning: Design Principles and Open Challenges”
Christian Cruz and Takeo Igarashi · 2020
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“D4RL: Datasets for Deep Data-Driven Reinforcement Learning”
Justin Fu et al · 2020
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“RL Unplugged: Benchmarks for Offline Reinforcement Learning”
Caglar Gulcehre et al · 2020
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“Morel: Model-based Offline Reinforcement Learning”
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli and Thorsten Joachims · 2020
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“Conservative Q-Learning for Offline Reinforcement Learning”
Aviral Kumar, Aurick Zhou, G. Tucker and Sergey Levine · 2020
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“Learning Quadrupedal Locomotion over Challenging Terrain”
Joonho Lee et al · 2020
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Pete Florence et al · 2021
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“ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning”
Ryan Hoque et al · 2021
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“Offline Reinforcement Learning with Implicit Q-Learning”
Ilya Kostrikov, Ashvin Nair and Sergey Levine · 2021
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“PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training”
Kimin Lee, Laura Smith and P. Abbeel · 2021
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“What Matters in Learning from Offline Human Demonstrations for Robot Manipulation”
Ajay Mandlekar et al · 2021
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“AWAC: Accelerating Online Reinforcement Learning with Offline Datasets”
Ashvin Nair, Abhishek Gupta, Murtaza Dalal and Sergey Levine · 2021
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“Behavioral Cloning from Noisy Demonstrations”
Fumihiro Sasaki and Ryota Yamashina · 2021
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“Intervention Aware Shared Autonomy”, 2021
Weihao Tan et al · 2021
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“Combo: Conservative Offline Model-Based Policy Optimization”
Tianhe Yu et al · 2021
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“Learning Reward Functions from Diverse Sources of Human Feedback: Optimally Integrating Demonstrations and Preferences”
Erdem Bıyık et al · 2022
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“Interactive Imitation Learning in Robotics: A Survey”
Carlos Celemin et al · 2022
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“When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?”
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“Modeling Human Response to Robot Errors for Timely Error Detection”
Maia Stiber, Russell Taylor and Chien-Ming Huang · 2022
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“Skill Preferences: Learning to Extract and Execute Robotic Skills from Human Feedback”
Xiaofei Wang et al · 2022
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Haoran Xu, Xianyuan Zhan, Honglei Yin and Huiling Qin · 2022
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