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The advancements in embodied AI are increasingly enabling robots to tackle complex real-world tasks, such as household manipulation.
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Rt-1: Robotics transformer for real-world control at scale
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Bc-z: Zero-shot task generalization with robotic imitation learning
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Calvin: A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks
Oier Mees, Lukas Hermann, Erick Rosete-Beas, and Wolfram Burgard · 2022
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R3m: A universal visual representation for robot manipulation
Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, and Abhinav Gupta · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Daydreamer: World models for physical robot learning
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Armbench: An object-centric benchmark dataset for robotic manipulation
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Plex: Making the most of the available data for robotic manipulation pretraining
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Bridgedata v2: A dataset for robot learning at scale
Homer Rich Walke, Kevin Black, Tony Z Zhao, Quan Vuong, Chongyi Zheng, Philippe Hansen-Estruch, Andre Wang He, Vivek Myers, Moo Jin Kim, Max Du, et al · 2023
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Robotube: Learning household manipulation from human videos with simulated twin environments
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Learning fine-grained bimanual manipulation with low-cost hardware
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Affordances from human videos as a versatile representation for robotics
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R3m: A universal visual representation for robot manipulation
Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, and Abhinav Gupta · 2023
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