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If generalist robots are to operate in truly unstructured environments, they need to be able to recognize and reason about novel objects and scenarios.
Adam: A method for stochastic optimization
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Denoising diffusion probabilistic models
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Alex X Lee, Anusha Nagabandi, Pieter Abbeel, and Sergey Levine · 2020
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Language conditioned imitation learning over unstructured data
Corey Lynch and Pierre Sermanet · 2020
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Offline reinforcement learning as one big sequence modeling problem
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Mdetr-modulated detection for end-to-end multi-modal understanding
Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion · 2021
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Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Offline reinforcement learning from images with latent space models
Rafael Rafailov, Tianhe Yu, A. Rajeswaran, and Chelsea Finn · 2021
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Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, and Chelsea Finn · 2021
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Rt-1: Robotics transformer for real-world control at scale
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Learning universal policies via text-guided video generation
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Scaling instruction-finetuned language models
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Ego4d: Around the world in 3,000 hours of egocentric video
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Classifier-free diffusion guidance
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Cacti: A framework for scalable multi-task multi-scene visual imitation learning
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Simple open-vocabulary object detection
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