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- W4324016657 abstract "We propose to learn tasks directly from visual demonstrations by learning to predict the outcome of human and robot actions on an environment. We enable a robot to physically perform a human demonstrated task without knowledge of the thought processes or actions of the human, only their visually observable state transitions. We evaluate our approach on two table-top, object manipulation tasks and demonstrate generalisation to previously unseen states. Our approach reduces the priors required to implement a robot task learning system compared with the existing approaches of Learning from Demonstration, Reinforcement Learning and Inverse Reinforcement Learning." @default.
- W4324016657 created "2023-03-14" @default.
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- W4324016657 date "2017-03-07" @default.
- W4324016657 modified "2023-09-27" @default.
- W4324016657 title "What Would You Do? Acting by Learning to Predict" @default.
- W4324016657 doi "https://doi.org/10.48550/arxiv.1703.02658" @default.
- W4324016657 hasPublicationYear "2017" @default.
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