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We focus on the self-supervised discovery of manipulation concepts that can be adapted and reassembled to address various robotic tasks.
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Towards generalized manipulation learning through grasp mechanics-based features and self-supervision
Andrew S. Morgan, Walter G. Bircher, and Aaron M. Dollar · 2021
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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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Transporter networks: Rearranging the visual world for robotic manipulation
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Yifeng Zhu, Peter Stone, and Yuke Zhu · 2022
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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
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Maniskill2: A unified benchmark for generalizable manipulation skills
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Grounded decoding: Guiding text generation with grounded models for robot control
Wenlong Huang, Fei Xia, Dhruv Shah, Danny Driess, Andy Zeng, Yao Lu, Pete Florence, Igor Mordatch, Sergey Levine, Karol Hausman, et al · 2023
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Lucy Xiaoyang Shi, Archit Sharma, Tony Z Zhao, and Chelsea Finn · 2023
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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
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Towards learning geometric eigen-lengths crucial for fitting tasks
Yijia Weng, Kaichun Mo, Ruoxi Shi, Yanchao Yang, and Leonidas Guibas · 2023
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Xskill: Cross embodiment skill discovery
Mengda Xu, Zhenjia Xu, Cheng Chi, Manuela Veloso, and Shuran Song · 2023
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