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Reinforcement learning (RL) requires either manually specifying a reward function, which is often infeasible, or learning a reward model from a large amount of human feedback, which is often very expensive.
MuJoCo: A physics engine for model-based control
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Can foundation models perform zero-shot task specification for robot manipulation?
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LAION-5B: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade W Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al · 2022
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CLIPort: What and where pathways for robotic manipulation
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Improving code generation by training with natural language feedback, 2023
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Training language models to follow instructions with human feedback
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Training language models with language feedback, 2022
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Eureka: Human-level reward design via coding large language models
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