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Many robot demonstration datasets contain heterogeneous demonstrations of varying quality.
Discovering imitation strategies through categorization of multi-dimensional data
A. Billard, Y. Epars, Gordon Cheng, and S. Schaal · 2003
Earlier work this paper cites.
A survey of robot learning from demonstration
Brenna D. Argall, Sonia Chernova, Manuela Veloso, and Brett Browning · 2009
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Uncertainty-Aware Reinforcement Learning for Collision Avoidance
Gregory Kahn, Adam Villaflor, Vitchyr Pong, Pieter Abbeel, and Sergey Levine · 2017
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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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Aviral Kumar, Xue Bin Peng, and Sergey Levine · 2019
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Reinforcement learning upside down: Don’t predict rewards–just map them to actions
Juergen Schmidhuber · 2019
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Learning with bad training data via iterative trimmed loss minimization
Yanyao Shen and Sujay Sanghavi · 2019
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Imitation Learning from Imperfect Demonstration
Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, and Masashi Sugiyama · 2019
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Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems, November 2020
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
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Heterogeneous learning from demonstration
Rohan Paleja and Matthew Gombolay · 2020
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Recent Advances in Robot Learning from Demonstration
Harish Ravichandar, Athanasios S. Polydoros, Sonia Chernova, and Aude Billard · 2020
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Behavioral Cloning from Noisy Demonstrations
Fumihiro Sasaki and Ryota Yamashina · 2020
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Variational Imitation Learning with Diverse-quality Demonstrations
Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, and Masashi Sugiyama · 2020
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Learning to Weight Imperfect Demonstrations
Yunke Wang, Chang Xu, Bo Du, and Honglak Lee · 2021
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Learning Latent Representations to Influence Multi-Agent Interaction
Annie Xie, Dylan Losey, Ryan Tolsma, Chelsea Finn, and Dorsa Sadigh · 2021
Cited alongside, same era.
Imitation Learning by Estimating Expertise of Demonstrators
Mark Beliaev, Andy Shih, Stefano Ermon, Dorsa Sadigh, and Ramtin Pedarsani · 2022
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Deduplicating training data makes language models better
Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, and Nicholas Carlini · 2022
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What Matters in Learning from Offline Human Demonstrations for Robot Manipulation
Ajay Mandlekar, Danfei Xu, Josiah Wong, Soroush Nasiriany, Chen Wang, Rohun Kulkarni, Li Fei-Fei, Silvio Savarese, Yuke Zhu, and Roberto Martín-Martín · 2022
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Writing system and speaker metadata for 2,800+ language varieties
Daan van Esch, Tamar Lucassen, Sebastian Ruder, Isaac Caswell, and Clara Rivera · 2022
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RoboAgent: Generalization and Efficiency in Robot Manipulation via Semantic Augmentations and Action Chunking
Homanga Bharadhwaj, Jay Vakil, Mohit Sharma, Abhinav Gupta, Shubham Tulsiani, and Vikash Kumar · 2024
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$ π _ \pi\_ 0$: A Vision-Language-Action Flow Model for General Robot Control, November 2024
Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, Szymon Jakubczak, Tim Jones, Liyiming Ke, Sergey Levine, Adrian Li-Bell, Mohith Mothukuri, Suraj Nair, Karl Pertsch, Lucy Xiaoyang Shi, James Tanner, Quan Vuong, Anna Walling, Haohuan Wang, and Ury Zhilinsky · 2024
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Lerobot: State-of-the-art machine learning for real-world robotics in pytorch
Remi Cadene, Simon Alibert, Alexander Soare, Quentin Gallouedec, Adil Zouitine, and Thomas Wolf · 2024
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Limited Preference Aided Imitation Learning from Imperfect Demonstrations
Xingchen Cao, Fan-Ming Luo, Junyin Ye, Tian Xu, Zhilong Zhang, and Yang Yu · 2024
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Efficient Data Collection for Robotic Manipulation via Compositional Generalization, March 2024
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Discriminator-Weighted Offline Imitation Learning from Suboptimal Demonstrations
Haoran Xu, Xianyuan Zhan, Honglei Yin, and Huiling Qin · 2022
Cited alongside, same era.
Data Quality in Imitation Learning
Suneel Belkhale, Yuchen Cui, and Dorsa Sadigh · 2023
Cited alongside, same era.
Robocat: A self-improving foundation agent for robotic manipulation
Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao, Coline Devin, Alex X Lee, Maria Bauza, Todor Davchev, Yuxiang Zhou, Agrim Gupta, Akhil Raju, et al · 2023
Cited alongside, same era.
Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets
Maximilian Du, Suraj Nair, Dorsa Sadigh, and Chelsea Finn · 2023
Cited alongside, same era.
DROID: Learning from Offline Heterogeneous Demonstrations via Reward-Policy Distillation
Sravan Jayanthi, Letian Chen, Nadya Balabanska, Van Duong, Erik Scarlatescu, Ezra Ameperosa, Zulfiqar Haider Zaidi, Daniel Martin, Taylor Keith Del Matto, Masahiro Ono, and Matthew Gombolay · 2023
Cited alongside, same era.
Learning to Discern: Imitating Heterogeneous Human Demonstrations with Preference and Representation Learning
Sachit Kuhar, Shuo Cheng, Shivang Chopra, Matthew Bronars, and Danfei Xu · 2023
Cited alongside, same era.
Learning and Retrieval from Prior Data for Skill-based Imitation Learning
Soroush Nasiriany, Tian Gao, Ajay Mandlekar, and Yuke Zhu · 2023
Cited alongside, same era.
Jensen Gao, Annie Xie, Ted Xiao, Chelsea Finn, and Dorsa Sadigh · 2024
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BAKU: An Efficient Transformer for Multi-Task Policy Learning
Siddhant Haldar, Zhuoran Peng, and Lerrel Pinto · 2024
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ReMix: Optimizing Data Mixtures for Large Scale Imitation Learning
Joey Hejna, Chethan Anand Bhateja, Yichen Jiang, Karl Pertsch, and Dorsa Sadigh · 2024
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Behavior Generation with Latent Actions, March 2024
Seungjae Lee, Yibin Wang, Haritheja Etukuru, H. Jin Kim, Nur Muhammad Mahi Shafiullah, and Lerrel Pinto · 2024
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Bidirectional Decoding: Improving Action Chunking via Closed-Loop Resampling, December 2024
Yuejiang Liu, Jubayer Ibn Hamid, Annie Xie, Yoonho Lee, Maximilian Du, and Chelsea Finn · 2024
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QueST: Self-Supervised Skill Abstractions for Learning Continuous Control, September 2024
Atharva Mete, Haotian Xue, Albert Wilcox, Yongxin Chen, and Animesh Garg · 2024
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Equivariant Diffusion Policy
Dian Wang, Stephen Hart, David Surovik, Tarik Kelestemur, Haojie Huang, Haibo Zhao, Mark Yeatman, Jiuguang Wang, Robin Walters, and Robert Platt · 2024
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A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges
Maryam Zare, Parham M. Kebria, Abbas Khosravi, and Saeid Nahavandi · 2024
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3D Diffusion Policy, March 2024
Yanjie Ze, Gu Zhang, Kangning Zhang, Chenyuan Hu, Muhan Wang, and Huazhe Xu · 2024
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PRISE: LLM-Style Sequence Compression for Learning Temporal Action Abstractions in Control
Ruijie Zheng, Ching-An Cheng, Hal Daumé Iii, Furong Huang, and Andrey Kolobov · 2024
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