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Imitation learning has shown great potential for enabling robots to acquire complex manipulation behaviors.
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Lerrel Pinto and Abhinav Gupta · 2016
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Ahmed Hussein, Mohamed Gaber, Eyad Elyan and Chrisina Jayne · 2017
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“Deep imitation learning for complex manipulation tasks from virtual reality teleoperation”
Tianhao Zhang et al · 2018
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“Neural task graphs: Generalizing to unseen tasks from a single video demonstration”
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“Learning to coordinate manipulation skills via skill behavior diversification”
Youngwoon Lee, Jingyun Yang and Joseph Lim · 2019
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Tanmay Shankar, Shubham Tulsiani, Lerrel Pinto and Abhinav Gupta · 2019
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“Accelerating reinforcement learning with learned skill priors”
Karl Pertsch, Youngwoon Lee and Joseph Lim · 2021
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Karl Pertsch, Youngwoon Lee, Yue Wu and Joseph Lim · 2021
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Anthony Brohan et al · 2022
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Aviral Kumar et al · 2022
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Oier Mees, Lukas Hermann, Erick Rosete-Beas and Wolfram Burgard · 2022
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Ajay Mandlekar et al · 2020
Cited alongside, same era.
“Iris: Implicit reinforcement without interaction at scale for learning control from offline robot manipulation data”
Ajay Mandlekar et al · 2020
Cited alongside, same era.
“Planning from pixels using inverse dynamics models”
Keiran Paster, Sheila McIlraith and Jimmy Ba · 2020
Cited alongside, same era.
“Ridm: Reinforced inverse dynamics modeling for learning from a single observed demonstration”
Brahma Pavse et al · 2020
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“Learning robust manipulation tasks involving contact using trajectory parameterized probabilistic principal component analysis”
Cristian Perico, Joris de Schutter and Erwin Aertbeliën · 2020
Cited alongside, same era.
“Recent advances in robot learning from demonstration”
Harish Ravichandar, Athanasios Polydoros, Sonia Chernova and Aude Billard · 2020
Cited alongside, same era.
“Learning robot skills with temporal variational inference”
Tanmay Shankar and Abhinav Gupta · 2020
Cited alongside, same era.
Soroush Nasiriany, Tian Gao, Ajay Mandlekar and Yuke Zhu · 2022
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“Augmenting reinforcement learning with behavior primitives for diverse manipulation tasks”
Soroush Nasiriany, Huihan Liu and Yuke Zhu · 2022
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“How to Leverage Unlabeled Data in Offline Reinforcement Learning”
Tianhe Yu et al · 2022
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“VIOLA: Imitation Learning for Vision-Based Manipulation with Object Proposal Priors”
Yifeng Zhu, Abhishek Joshi, Peter Stone and Yuke Zhu · 2022
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“Bottom-up skill discovery from unsegmented demonstrations for long-horizon robot manipulation”
Yifeng Zhu, Peter Stone and Yuke Zhu · 2022
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“Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation”
David Brandfonbrener, Ofir Nachum and Joan Bruna · 2023
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“Predicting Object Interactions with Behavior Primitives: An Application in Stowing Tasks”
Haonan Chen et al · 2023
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“Diffusion Policy: Visuomotor Policy Learning via Action Diffusion”
Cheng Chi et al · 2023
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“Multi-Stage Cable Routing through Hierarchical Imitation Learning”
Jianlan Luo et al · 2023
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“Waypoint-Based Imitation Learning for Robotic Manipulation”
Lucy Shi, Archit Sharma, Tony Zhao and Chelsea Finn · 2023
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“Mimicplay: Long-horizon imitation learning by watching human play”
Chen Wang et al · 2023
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“Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware”
Tony Zhao, Vikash Kumar, Sergey Levine and Chelsea Finn · 2023
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“Semi-supervised offline reinforcement learning with action-free trajectories”
Qinqing Zheng, Mikael Henaff, Brandon Amos and Aditya Grover · 2023
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“Learning universal policies via text-guided video generation”
Yilun Du et al · 2024
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