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arXiv · 1703.02658

What Would You Do? Acting by Learning to Predict

Adam Tow·Niko Sünderhauf·Sareh Shirazi·Michael Milford·Jürgen Leitner
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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.

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Adam Tow, Niko Sünderhauf, Sareh Shirazi, Michael Milford, Jürgen Leitner. 2017-03-08. What Would You Do? Acting by Learning to Predict. https://arxiv.org/abs/1703.02658

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