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Improving Reward Learning by Estimating Annotator Expertise
Reinforcement learning from human feedback (RLHF) has been used successfully to teach robots tasks that are difficult to specify …
Pavel Czempin, Rachel Freedman, Ellen Novoseller, Vernon Lawhern, Cameron Allen, Erdem Bıyık
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Clam: Continuous latent action models for robot learning from unlabeled demonstrations
Learning robot policies using imitation learning requires collecting large amounts of costly action-labeled expert demonstrations, …
Anothy Liang, Pavel Czempin, Matthew Hong, Yutai Zhou, Erdem Bıyık, Stephen Tu
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In-Context Generalization to New Tasks From Unlabeled Observation Data
Large pretrained models in natural language processing and computer vision have achieved impressive capabilities by training on vast …
Anthony Liang, Pavel Czempin, Yutai Zhou, Stephen Tu, Erdem Bıyık
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In-Context Generalization to New Tasks From Unlabeled Observation Data