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Learning from human demonstrations is an emerging trend for designing intelligent robotic systems.
Learning from demonstration
Stefan Schaal · 1996
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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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Concept2robot: Learning manipulation concepts from instructions and human demonstrations
Lin Shao, Toki Migimatsu, Qiang Zhang, Karen Yang, and Jeannette Bohg · 2021
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Robot task planning and situation handling in open worlds
Yan Ding, Xiaohan Zhang, Saeid Amiri, Nieqing Cao, Hao Yang, Chad Esselink, and Shiqi Zhang · 2022
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Dexmv: Imitation learning for dexterous manipulation from human videos
Yuzhe Qin, Yueh-Hua Wu, Shaowei Liu, Hanwen Jiang, Ruihan Yang, Yang Fu, and Xiaolong Wang · 2022
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Reflect: Summarizing robot experiences for failure explanation and correction
Zeyi Liu, Arpit Bahety, and Shuran Song · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
Earlier work this paper cites.
Perceiver-actor: A multi-task transformer for robotic manipulation
Mohit Shridhar, Lucas Manuelli, and Dieter Fox · 2023
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Rt-2: Vision-language-action models transfer web knowledge to robotic control
Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choromanski, Tianli Ding, Danny Driess, Avinava Dubey, Chelsea Finn, et al · 2023
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Task and motion planning with large language models for object rearrangement
Yan Ding, Xiaohan Zhang, Chris Paxton, and Shiqi Zhang · 2023
Cited alongside, same era.
Do as i can, not as i say: Grounding language in robotic affordances
Anthony Brohan, Yevgen Chebotar, Chelsea Finn, Karol Hausman, Alexander Herzog, Daniel Ho, Julian Ibarz, Alex Irpan, Eric Jang, Ryan Julian, et al · 2023
Cited alongside, same era.
Progprompt: Generating situated robot task plans using large language models
Ishika Singh, Valts Blukis, Arsalan Mousavian, Ankit Goyal, Danfei Xu, Jonathan Tremblay, Dieter Fox, Jesse Thomason, and Animesh Garg · 2023
Dinov2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
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Demo2code: From summarizing demonstrations to synthesizing code via extended chain-of-thought
Yuki Wang, Gonzalo Gonzalez-Pumariega, Yash Sharma, and Sanjiban Choudhury · 2023
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Vlmimic: Vision language models are visual imitation learner for fine-grained actions
Guangyan Chen, Meiling Wang, Te Cui, Yao Mu, Haoyang Lu, Tianxing Zhou, Zicai Peng, Mengxiao Hu, Haizhou Li, Li Yuan, et al · 2024
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Gpt-4v (ision) for robotics: Multimodal task planning from human demonstration
Naoki Wake, Atsushi Kanehira, Kazuhiro Sasabuchi, Jun Takamatsu, and Katsushi Ikeuchi · 2024
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Cited alongside, same era.
Gpt-4v (ision) for robotics: Multimodal task planning from human demonstration
Naoki Wake, Atsushi Kanehira, Kazuhiro Sasabuchi, Jun Takamatsu, and Katsushi Ikeuchi · 2023
Cited alongside, same era.
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick · 2023
Cited alongside, same era.
Grounding dino: Marrying dino with grounded pre-training for open-set object detection
Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, et al · 2023
Cited alongside, same era.
Yao Mu, Junting Chen, Qinglong Zhang, Shoufa Chen, Qiaojun Yu, Chongjian Ge, Runjian Chen, Zhixuan Liang, Mengkang Hu, Chaofan Tao, et al · 2024
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Graphmimic: Graph-to-graphs generative modeling from videos for policy learning
Guangyan Chen, Te Cui, Meiling Wang, Chengcai Yang, Mengxiao Hu, Haoyang Lu, Yao Mu, Zicai Peng, Tianxing Zhou, Xinran Jiang, Yi Yang, and Yufeng Yue · 2025
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Fmimic: Foundation models are fine-grained action learners from human videos
Guangyan Chen, Meiling Wang, Te Cui, Yao Mu, Haoyang Lu, Zicai Peng, Mengxiao Hu, Tianxing Zhou, Mengyin Fu, Yi Yang, and Yufeng Yue · 2025
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